{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# k-Nearest Neighbor (kNN) exercise\n",
    "\n",
    "*Complete and hand in this completed worksheet (including its outputs and any supporting code outside of the worksheet) with your assignment submission. For more details see the [assignments page](http://vision.stanford.edu/teaching/cs231n/assignments.html) on the course website.*\n",
    "\n",
    "The kNN classifier consists of two stages:\n",
    "\n",
    "- During training, the classifier takes the training data and simply remembers it\n",
    "- During testing, kNN classifies every test image by comparing to all training images and transfering the labels of the k most similar training examples\n",
    "- The value of k is cross-validated\n",
    "\n",
    "In this exercise you will implement these steps and understand the basic Image Classification pipeline, cross-validation, and gain proficiency in writing efficient, vectorized code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Run some setup code for this notebook.\n",
    "from __future__ import print_function\n",
    "import random\n",
    "import numpy as np\n",
    "from cs231n.data_utils import load_CIFAR10\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "\n",
    "# This is a bit of magic to make matplotlib figures appear inline in the notebook\n",
    "# rather than in a new window.\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots\n",
    "plt.rcParams['image.interpolation'] = 'nearest' # 画图差值方法\n",
    "plt.rcParams['image.cmap'] = 'gray' # 图片保存为灰度图\n",
    "\n",
    "# Some more magic so that the notebook will reload external python modules;\n",
    "# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n",
    "# 自动加载发生变化的模块\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training data shape:  (50000, 32, 32, 3)\n",
      "Training labels shape:  (50000,)\n",
      "Test data shape:  (10000, 32, 32, 3)\n",
      "Test labels shape:  (10000,)\n"
     ]
    }
   ],
   "source": [
    "# Load the raw CIFAR-10 data.\n",
    "cifar10_dir = 'cs231n/datasets/cifar-10-batches-py'\n",
    "X_train, y_train, X_test, y_test = load_CIFAR10(cifar10_dir)\n",
    "\n",
    "# As a sanity check, we print out the size of the training and test data.\n",
    "print('Training data shape: ', X_train.shape)\n",
    "print('Training labels shape: ', y_train.shape)\n",
    "print('Test data shape: ', X_test.shape)\n",
    "print('Test labels shape: ', y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(50000, 32, 32, 3) <class 'numpy.ndarray'>\n",
      "(50000,) <class 'numpy.ndarray'>\n",
      "(10000, 32, 32, 3) <class 'numpy.ndarray'>\n",
      "(10000,) <class 'numpy.ndarray'>\n"
     ]
    }
   ],
   "source": [
    "# Mine\n",
    "cifar10_dir = 'cs231n/datasets/cifar-10-batches-py/'\n",
    "X_train, y_train, X_test, y_test = load_CIFAR10(cifar10_dir)\n",
    "print(X_train.shape,type(X_train))\n",
    "print(y_train.shape,type(y_train))\n",
    "print(X_test.shape,type(X_test))\n",
    "print(y_test.shape,type(y_test))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Visualize some examples from the dataset.\n",
    "# We show a few examples of training images from each class.\n",
    "classes = ['plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']\n",
    "num_classes = len(classes)\n",
    "samples_per_class = 7\n",
    "for y, cls in enumerate(classes):\n",
    "    idxs = np.flatnonzero(y_train == y)\n",
    "    idxs = np.random.choice(idxs, samples_per_class, replace=False)\n",
    "    for i, idx in enumerate(idxs):\n",
    "        plt_idx = i * num_classes + y + 1\n",
    "        plt.subplot(samples_per_class, num_classes, plt_idx)\n",
    "        plt.imshow(X_train[idx].astype('uint8'))\n",
    "        plt.axis('off')\n",
    "        if i == 0:\n",
    "            plt.title(cls)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x98c2b00>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x99322b0>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'plane')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x9945b70>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x99bb390>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x99bb4a8>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x99dd828>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x99dda20>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x9a04e80>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x99ddb38>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x9a35748>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x9a357b8>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x9a5cf60>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x9a692b0>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x9a8c710>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x9a8c908>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x9ab6e10>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'car')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x9a8ca90>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x9ae7630>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x9ae78d0>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x9b0ff98>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x9b1d128>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x9b418d0>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x9b41c18>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x9d69080>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x9d69198>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x9d897b8>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x9d89b38>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x9dbd0f0>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x9dbd4a8>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x9de3908>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'bird')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x9dd3278>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x9e1b240>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x9e1b048>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x9e3da58>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x9e3dac8>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x9e70208>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x9e70358>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0xa0b7ac8>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xa0b7d68>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0xa0e9320>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xa0e93c8>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0xa1109b0>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xa110eb8>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0xa144390>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'cat')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xa127cf8>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0xb19abe0>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xb19ac88>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0xb1ca438>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xb1ca6d8>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0xb1f3c50>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xb1f3fd0>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0xb2263c8>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xb226518>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0xb24fcc0>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xb24fd68>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0xb2824a8>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xb2826d8>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0xb2a7c88>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'deer')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xb2a7f98>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0xb4c95c0>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xb4c9908>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x95f8470>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x95f8198>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x9835278>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x95f8320>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x8c7fc88>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x8c7f828>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x9024278>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x9024eb8>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x98c2e48>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x98c2ba8>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x980ba20>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'dog')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x980b3c8>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x990dc50>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x9917898>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x83aac88>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x83aac50>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0xa1a3668>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xa1a36a0>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0xb4fdf28>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xa1a3780>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x97e86a0>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x97e87f0>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x860df60>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x86062b0>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x95db7b8>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'frog')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x95db828>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x95f1f28>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x96020f0>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x9597860>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x9597b00>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x964bf98>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x963d400>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x963e8d0>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x963ea20>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x9564080>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x95643c8>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x9558940>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x9558c50>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x93a50b8>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'horse')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x93b3a20>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x94eb860>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x94ebc88>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x9276240>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x92762e8>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x9282d30>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x9282cc0>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x92312b0>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x9231550>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x90e9ac8>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x90e9be0>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x8e972e8>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x8e975f8>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x8ea2b38>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'ship')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x8e95400>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x90053c8>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x8ea2dd8>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x8f3fbe0>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x8f3fcc0>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x8b16438>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x8b166d8>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x8b46c50>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x8b46f98>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x94834a8>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x94835f8>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x9208cc0>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x91ff0b8>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x91234a8>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'truck')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x91d7da0>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x912afd0>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x9114080>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x916a5c0>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x916a908>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x8db2cf8>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x916a860>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x8d0b7f0>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x8d0b588>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x8cf9d68>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x8cf9c18>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.0, 0.0, 1.0)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x8e175f8>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXEAAAEICAYAAACpqsStAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvOIA7rQAAIABJREFUeJzsvXecZVd15/td+6SbQ6WuzlkdlAMCZBBIiIwD2P7YePyMZ5yzB6cB22N7DPbYn/FjHgMeP3uwPYwHR4yNbRhsgoSEQKDULbXUuapT5aqb78l7vz/ObVFquqtagRbyq9/ncz91z90n/Pbea6+99lprnxJjDGtYwxrWsIYXJ9QLTWANa1jDGtbw7LGmxNewhjWs4UWMNSW+hjWsYQ0vYqwp8TWsYQ1reBFjTYmvYQ1rWMOLGGtKfA1rWMMaXsR4QZW4iLxaRM6+kBy+USAikyJy10V+f6WIHHmG9/pTEXnP88fuhcM3Yl1eCE4iskdEHhGRjoj89BV65kVl8hsVIvLrIvJnK5QfEpFXX0FKzxgiYkRk1zO5Zs0S/waHMeZeY8yeF5rH5eLFNvBfRPhF4G5jTNkY8/4XmsyLEcaYq40xdz/X+3yjyfiaEn+eICL2/x+e+a8BL9J22woculiBiFhXmMtl40Xa1s8aL0R9r4gSH8xc7xKRJ0SkISJ/IiK5i5z3H0TkxGDJ+ISIvHVZ2feLyH0i8l8G95gQkTcuK6+KyIdEZFpEzonIe56JcIvIZhH5WxGZF5FFEfmAiOwUkc8OjhdE5H+LSO2Cev2SiBwEes9DB77kwja60OV0sWeKyI0i8vCg3f4S+Jq2fTZ4pm0iIv8L2AL8g4h0ReQXn8UzL1kXEXmLiDwqIk0RuV9ErltWtkFEPjrgOrHc5TBYZv+NiPyZiLSB738eOf2QiBwXkSUR+biIbFhW9joROSIiLRH5fRG5R0R+8Fm0yWeBO4APDNr1IyLy30XkEyLSA+4YyP+HB/U/JSK/IiJqcL0lIr836K8JEflJyZbtlyOvN4jIwUEd/vL8uF2l3kZEfkJEjgHHJMP7RGRucJ+DInLN4FxPsjF9WkRmReQPRCR/GW3yS5KN886gjV8zKHIH7dCRzH1yy7JrnrKgl8nEXw7OfVhErr+M536NjA/q+wMichr4rFzETXzBsy0Rebd8Vdc9JCKbL/KsV4jIGRG5Y0VSxpiv+weYBB4HNgNDwBeA9wCvBs4uO+87gQ1kk8t3AT1g/aDs+4EY+CHAAn4MmAJkUP53wP8LFIEx4MvAj1wmPws4ALxvcH0OeAWwC3gt4AGjwOeB/3pBvR4d1Ct/hdroac8EXOAU8O8BB/iOQTu95znyeS5tctezfOYl6wLcBMwBLx1we8fgWd5AXh4C/uPgHjuAk8DrB/f99cF9vm1w7mX31Sqc7gQWBtw84L8Bnx9cNwK0gbcBNvAzg+t+8Fm2zd3nrwX+FGgB3zSoTw74MPD3QBnYBhwFfmBw/o8CTwCbgDrwacAA9mXI5JfJxuQQ8OTgXpes9+A6A/zL4Jo88PpB/9QAAfbx1XH9X4GPD84tA/8A/PYqvPYAZ4ANg+NtwM5BPwfAmwYy8tvAly4mm8tk4jsG/frzwATgXOZYvWvZs82g/YuD+r6aZWP2Itf8AvDYoB4CXA8ML2u7XYM2OwPcuiqf5zLQn6GC+tFlx28CTlysshdc9yjwrYPv3w8cX1ZWGFR4HFgHhCwbnMDbgc9dJr+XA/OXIdTfBjxyQb3+3ZVsowufCdzOssls8Nv9PHcl/lza5Nkq8UvWBfjvwG9ecP4R4FVkiv30BWXvAv5k8P3XWaZknkdOHwJ+d9nvJTLFsA34PuCLy8pkMCifLyX+4WVl1kD+9y/77UfIfOgAn2WZQQPcxeUr8e9ddvy7wB+sVO/BsQHuXFZ+J9mk8jJAXdAmPWDnBXI3sQqvXWQT+l0sU7qDfv70suP9gH8x2Rycu1zBK2AaeOVl9MXy+2wb1HfHsvJXs7ISP8JAr13k3mYgu6eAay9HNq6k/+bMsu+nyGb3p0FEvg94J1nDQCYcI8tOmTn/xRjTF5Hz5wyRzabTg98g65Tlz1wJm4FTxpjkAj5jwPuBV5JZCQporFCv54pV2+gi520AzpmBBCy79rniubTJs8VKddkKvENEfmpZmTu4JgU2iEhzWZkF3Lvs+Nn200qcNgAPn//RGNMVkUVg46DszLIyc+ES+zlieX1G+OqKYTnHjct4Lj//mbTFzLLv/cG9hrl0vScvfIYx5rMi8gHgg8AWEfkYmeWbIzPGHlo2boWs7y4JY8xxEflZMkV8tYh8ikxvXIxvTkTsC+X4Ihz1oH8uNeZWwzNp081kBtql8LNkk/Rjl3OzKxnYXO7z2UJm3TwFEdkK/BHwk2RLixqZe0FYHWfILJERY0xt8KkYY66+TG5nyITrwkntt8lmxuuMMRXgey/C5/l8DeSKbXSJZ04DG2XZKBhc+1zxbNvkubTHSnU5A7x3Wf/WjDEFY8yfD8omLigrG2Pe9DzwWonTFNnkAoCIFMkU3LnBdZuWlcny4+cBy+uzQGYJb13225YBDy7kwtPl7NlgpXpfjB/GmPcbY24GrgauInMpLAA+cPWyfqsaY0qrETDGfMQY84oBDwP8zrOox1PtMIgfbOLSY+5pj1/ltx7Z5HT+3haZ6/E8zpC5fy6F7wS+bTBRrYorqcR/QkQ2icgQ8G7gLy8oL5I1xDyAiPxb4JrLubExZhr4Z+D3RKQiIkqyANyrLpPbl8kE/T+LSFGygOI3kVmaXaApIhvJBO/ridXa6GL4IpAAPy1ZkPNtwK3PA5dn2yazZD7pZ4OV6vJHwI+KyEsHgbKiiLxZRMoDru1BsCs/CBxdIyIveZY8LpfTR4B/KyI3iIgH/BbwgDFmEvgn4FoR+bbBRPgTZK6/5x3GmBT4K+C9IlIeGETvBM7nTP8V8DMislGyIPQvPcdHrlTvr4GIvGTQbw6ZgguA1Bijyfr1fYMVHgOOr1/p4ZLlzN85eHZANhGkz6IeN4vI2wb987NkhuCXLuO61WT8KNkK4M2DOv8KWezgPP4H8Jsisnsgy9eJyPCy8ingNWQy9+OrkbmSSvwjZIr25ODztM0SxpgngN8jGzSzwLVkwb3LxfeRLSmfIFve/w2w/nIuHAyCbybztZ0GzpIFVn+DLHjTIhuUf/sM+DwbrNhGF4MxJiILnn0/Wb2/i+eB53Nok98GfkWyDJKff4bPvGRdjDEPkgW1PzAoOz44bznXG8iCUwtkA6X6TJ7/LDh9BvhV4KNkE95O4LsHZQtkFtXvAotk/tkHyRTF1wM/RaYgTwL3kcnSHw/K/ohMrg4CjwCfIJuYno3iW7Hel0BlwKFB5uZZBP7LoOyXyPryS5JlDn2aLOC3EjzgP5P18wxZIsO7n0VV/p6sPxvA/wW8zRgTX8Z1T8k4WWD0aTDGtIAfJ5PBc2T9styV9n+TTaz/TBb8/hBZQHT5PU6TKfJfklUyms5ndnxdISKTZEGZT3/dH7aGNXwDYrBcPwv8G2PM515gLm8E/sAYs3XVk/+VQkR+HdhljPneF5rLc8XaZp81rOHrBBF5vYjUBsv+d5PFDi5nuf5888iLyJsG7qCNwK8BH7vSPNbw9cGaEl/DGr5+eDlZFsICmbvn24wx/gvAQ8jcYA0yd8qTZDn1a/hXgCviTlnDGtawhjV8fbBmia9hDWtYw4sYV/RlLZNzDWOMQYlg2zZKZXOIiGCM4X/+6R/zute9jm3bt0OqUUZADCKG82mYIvLU5zy01hgDRmf3QYR6OXfJ/PI7brzOrCuVuWnfTXzLK25j6xtey6nPfBhpH+EDf3+IU+EwjfkNtJYsQjPN/EyH//Sbb+F//emDzMdD1KM51u/ZyTf/2Pdx5MFHmJk8w9Z9t9DrztGauZ/h7S+hGzcpKsX7futHL8njTa/ebpy8Q65QxkmKkMaEOuTs3DnqwwUWFmbYv+d64mZCo9GjOlRH2TA0NEqUauLUYOIAv9+mG4Y4OdA6oODWiYMERzl0/T6J0Xzyc49dksd7f/U/Go3BCIhtkXM98rkcrutiWRae56FsF7FcbMvBdjwcr4Rlezj5Mk6lzvrxOqN1j4NHFui0O/i9JlHQIvK7+O0OJCFpEvFTP/XDl+QxcfhLJklS5IIzsn1sFmnY48iTT9LqBuTqxcGWw4QkSiDV5PI5lGtTsF0iNCKgRCGAwTA6NIqIjVcssu+621faf/DU8nRqaoq/+pu/YMemTezbtYMg9Dly8hQ3v+SlbN+xmzRNsayL7U05f4tVtzlc8oSPffIXzJ/9+d8x3wiZPL3E1ddvZ9vuIdKwR9hsM1Ksc+Lhs5iey3C1RtF2CYIQy7aJo4gwimh3eliWg1KKbq9PGCU4roPjODRaDWLLQ1yXrzx59pI8fuQP7jEMxtVT4xABAaUUrq1xbIWSAoIFEqOVxhhrUD1BjJCIoTX9GP1OyM69L6HbatNsLlLfuI00SbB0xPv+3SsuyePX//jLxg9CwkTTCxLSNCVNU4w2xGlCL/Qx2qCNJk1SdKRJkgRjDEmSkqQpSZodx1GM1hpt9Pmdkth2pg5FhAN/9fOX5PGOn3yH8X2ffNEwN9PgzJlZRsdq2J5CpzB3dpEIwUs1NQzFXJ5zfsLwumEi+mzYOs5YbZRus0+7GSAOaCtmcnKSSqVC2NX0en1E4LEvPrqqAF1RJR74PsYYlFLYto1lWURxwF//9V8zOTnBd377dzI6Okrg+1iisM7L90CRn1fc55X+cleQMQajs0lBY1jpHVAVx2L9+grf8gPfyYadu7BrFXYOG5z4CfYPBbSCdbTmAjwnRxwmWDbce/e9pLqKhUWhEJP3Fjj+6BEWF2dRro1XqWMVSkwc+QTtRz5HYkfs3rLy3o6lZpOyrlMtV2m1m+gkxnKFUrFCMV9liSa+H+L7IRrBdlw0GsTBmJQ4iUnjFD8MCaI+MYDRkEZEQcJwrUgOIdYrZ5LFSYwZtJsYjSUK27KeamfbcdBJgmiVTZRYiJWA2Kg0RZKEJE1Jk5Q4jkjiiDSOSeKYNInRaYLRCam+2Ka5r8K27adNzk+DsZBEMTU1w9TcAkt+g3avxUgxj2vn2TS+GT/0mVs8RyGXx9iCZYFjWajUIAZuvOFmyrVRvGJxRR6QGQYA733ve7n3vnv4Dz/3c1QqNRZOLvAbv/YbXLXvan7xP7yLW265hTRNM0NDG4LAJ01THCeb/FDu0+57yfpdjIMfsXCuyVIrYtt4lScePUNtpEypIhjHUIlcvJaiF6V00jZOpYpn24gSxLaxBEr5IihFY6lJuZjHdlJyOY9CPo/raRb7Eam9MiedJl/DXZRCISgRUqUQy8XDRbQixhCJRsRGjKCMQkmmyKXTxWlMcfbBc+QdIUod4vFN6FRw9MqOgbznIaKw4hQjCWmqSZI4m0hThRFI04FyVxZGGeLYQusUpRSiFSoVtDYg5185khmAkE1Il9M7r3nNbfR7IQcPHCA2S1SKBdozi0RJRK1ap+6WWAz7GMtww/hWxspV/u7ESXo9nyTt02902XPDy3jkgYd56fU38cTxJ0kch3q1Tq/XJfINYgxRGF0GmyusxM8PDGMMWmtEhCSJefjhr3Do0CF+5d2/jGV9bUeel53lQnShEofMOjCYVffm7dk8yq037mbTeBWvViLtzpDOncTpzXDdzhJ/8+lZelGMcqsY7WNcoewVGB1bR3uqQ7maUMnP0j91DzkFllUmWDxLOywxO9tgnZuSq+YorZJyWiwOYVs5XFXAy4doHARNycosnmpllKWlNhYe4rg4bo6e3yFFZx+TfRzPpbl4FqMMmzdsRZkcjtYEcZxZH0avyCOJE4xkSlwJJElMkmQrJaUUGJMpKpMiokiSGJUkKCv7nSQZWDqGOIpIohCdRJg0xug0m1hMCqzMY7lcPO33rBCQzIq0bfq9LknsY0uOOAqIophKuUy37+IHXeIgQZQmZztYicG1LKbPnMDL5S8qYxdCa41Siv/zyU9SqZfxAx/H9ThyYoJ+GHPvffcz88538sEPfpAbbrgBk0YsLi4wPTXF0NAQkd+nVq9THV2HkUzhPVNs2rCBl730Zqbn5yDXwCqntHuT4No4thB0G+zYtg7HGaLT6dDrdEhSg2UpXNsmVyiitSLVYNWrdPt9tAlxLKFaLhJHLXQSE6QrT67nVxrLLVUReUo+LGWR9NqcOf0g3dY8oxt2Uh7dSNBvUvAcOo15gm6LYnmIbV7A9a/YQ6vXplIpsmgqHOinkMujopXHS86zs9WVZaER0jQlUoYkNijRGCySxJAq0JagLUEEtBaUJUgqpClonRmF5+XtvB5ZbiSuhHq5ik2XemUIV01RGRvlXC8gTlOsVNCRhliTiGZs3XpyJjN8oiBm06aNvOb2V1J0cqRxyp5duylWipTHKihRdLoddAhJkhCGl7el4Ioq8eWK9ymXiOVQqg1THRnhV37t3fzKL/8q5XIVbbLZUkRQKMSATslmUCVoyVS2LNcLX13trYgtW7awdXwzi0dO4U22CWcPYSYOkNbHuGnbAktnHwI9iomh4Ayh6PPK/Xs4p7ZgPXKYpL3IXHOGdbk+8wsbKOQWOXmmQ6W2g11bN3P7K25jw2gO1V15Jt2wYTf9bhPXVszMz9ANe+Rsh0rRwUoNhfwYjutwbmqGndu30Ov3SU2I5WrioI8f9qiWKjSW+oxv3MhSY4mJU+eoFIfJ2TmaTZ84TRG1stKaXZjP2lkEL+cRuh5xnFDI54ijEIUG2wXboLVgtKHVWaBQDHELCW6q6RZd8rai3WoR+H102EfHIToKSJIQo2PMKsoi68JLDyDbcSi5iqg1B36XkUqR0eowxXKZ2cUFosjjtltewmy7wRNHDoNOiJOUfhSBEdKzp6mMjLDJXLUqD9u2+cM//EOCOCacnuWez97NyOgI7//DPyCwFOQ9JiYnec1r7+K33vNevuX1d3LyyGHWb9xAPmdRyBVZmp/GyXkUyzUM6qmVzeVa427eYd+1W4iPTjGVtBkrJ+TJoXxNoexgqn2qVQ9ztk9tpISMlcE4YCCKQ+I4IgwT7FyOwFLkcx5et4tgCHod6qVh2qEi6LZXbgsnW5WJUti2QiTBEou4H9JuzNM58yCnH7sPE3ZJ04Rk61UsVUYIuy3mpk6T+l1qeZc77rid2akF/vFAyPj6MW556Y3UokVU7BAEIbSngNsuyWO4ViGIYqLU4PkxcRKTJJnlnSQpcZpkx0lCqjWxNkRRRJJkbpdE68xS15ooijJ3yjIlfh5qlfHyP/7bX5AvGKanZ+m2uoin2bv/OsbHNnLTdddz7dXX0Y8StFYcmzjKwlKDn7jzzVQrJYqezdlTRxFL84rX3MXWPXu4+bZbKRQ8PMcFEQpuHqMyPXc5eCH+kcHTjh3boVgq4fs+J04e5yMf+d98x7d/F6Ojo1wsccYYMgU/+L7c93i5tk513XZOLmpONCa4dpfLuSPTjLcWKFkJJTQ7R12qwyF79xgefOQUS4/kSPwOO68f4ehphzPNmLgfY/pdRt0iDjO0mku0+w227y0w43fonDrLlmJ5RR5R3McPfVIliIKw1wW3yEh9HYVCDtfxcN08s9Kg02nSbC5QHyrT7XSJ/RgTxSwuztNu97nt5a9i8tQxHl96kH5vCVWsYIzG7/ex1MrdvNhqohBspcgHOdx8nnTwkrmcZ7B9H3ENllGIUphU4YeGSCs8rSiLwu+F9AoQhgE6CUjjgDToY9KIFIMCrFUGB2S+64v1pJD5OoOwhyUxtWKFKNT0Wn2UeCRxhFPKEycpzVabOIqJowjLtsFy8MMI6fv0+n2UrLwiAIiiiIMHD9Lt9Xj97bezb89ePv7xj3PVrt0sfPlhgjjGtiwSY/js3Xdz+83XkQR94n6fNIkQBcVigQfuvY+bbn0Z1ZGxp6z7y4XfDlmaaRP44LoerltluzVEWTn0iek3QqpxGVXKI6nBsS06vZhUG0oFD9/WuJ6NWC4tPyCOE6r5HEXPptUPaXQCtqwbIZ9feeQ4YqOUDNwNCq0VrpXSWpjk+CP3ofyz6LBFTgfYJiY4cwg9tplSqcT+nVuJ+wHbN23hVS9/NV+657N0rA6OMTxw7/1sGhqiXPOZPvAg7TOH4V0/c0keT7lTUoPGIk5skiRB6xStNVGSKfE4jomTBMeAZSmSgbsvThKSVAarPUOa6oE75ZkpcWUSNo3vIAkMOa9IGPZw+iHb6iOMj23moccfp9ntkSvWefChgzSW2gyNzCIoor7PxMlDWKbL3quvw/PWMXv6SRztM1IZplAqMFxfh1f1cMo5brlp5VeJwxVW4sstEaXUU9Zf3vHIux4z01N89KMf5VWvupOxsbGvul8YuEqMwWAY/IyIYNk2ysq857EfDu65Mo9tG7YytZRy7Ikvs6Fe4sSxSRxLGPYW8S2P/dsqFIdDbn9Vl4nJFmmqWUhDHvjMPzJ3/CxlpWjFgh/0ueW6USZP+qR6iSAIGB/ezdHJSfAb1Pav9I4bUOLiuAVGRjawsd8l8CPi2DA8vB6lBM9zOX3qLFprZudmKZXy+H6ETlvYdh6lbGwLSiVDpVSnVKjiOi62ZdHtdtGJRik7s3JWQNJcACWEgO/YWE6BfK9CVKtTqlQwlotnNG7Sp5c2sCwbPyyitCI2Cst2CMOIIAhJQx8rWEIlPiYNiZMEtMFoTZSssqPZZO4wIwZlBsHC8xO1aIyCgBBDwHhtI412gg5ikq5LzsmjLc3k2TMEfh9LpyjHQhQ4jofWCZbr0gl6NFszK7EAoNlsMjExQc61ef1r72JhYY7W0iI/+MM/wv1ffAAb0KnGshRHjx7l7LmzJEGA53mYJMX2HMS2OTUxyUKjzZvf+jYKhcKqz12Ou+95mHs+/zCLfpux8RzDeWHv9VvYNFJnptPnxMxZgkBh0Azl83hKiFREP44wxsHLlwjDCK0Ntg1JmFAplqhXKyjVp9kN2Di+Drew8v9OydlZxAQ0YgS0JvGbNGdOULJjbLdAogoYP8IkinKtRrfRpNvvUB5bzy0338DG8Y24SvAcgy5YPHLwURodn3+aPcWPf99P0irZTMjKsZti3sWyBCfVGEeRJBZJamEG1nQYhiSxEMUQxxAbENEktpDGCldZxNom0SkiMghspqAHiRNGAYJaRYEEfo/HDhyk1+uyddcuTh1bojc9h7XfcOzkNFP9hOn5Lls2VPEjCydf5qq9+4jDhLmpaRwvT8FxCIKEpaUW933ufvrTpxkqlkkx2E4B10lxXcWHPvENqMTP/z2vyI0xBP0+7WaLKI6wbZfPfvZz7N61O8ssGLhTDCBKsJSFbSu0MfT9PicmznL8+HEmJib4yR/6kYHrdOVO+NhH/gyrtIWtw3l6vZjy1lFUtI0kmSb1u9yyf5SphQXod7HiEUR8ZoOAXreFm3SxlMH1inSjiONnDtDs13HrG5idnmJ83TgHZls41Gl3V+ZRro5SLNUplOpUK6OsH/eZnDxNrx9QLJbo+wknJ0+TyztUa0V27d7L7NwMjpMjjRXVapU09fGDgMWlOVrtJoVCCdfN0W51aftNlNjYtrsij5I/gzgWGkOaKqKgSKvfIew1Cfoj2K6HSgGT4Dga1zHEQQsin8gXCBvUC0LF06SdOTzdIZ/36BlFHGkYWEqXuydBjEGbr2apyPkME20wtsXYaI3RQp28J+ScHCIw31+AWHA9GyMWVaVQXhZc9DyPrgPas1ESc/r0MW669KodgOPHj3P69GlqlRKFnEuv3STvOtQrJarFAgthk3RQn2ajQaVeY+5cB4xBtEbpzFjZuH4993/5Iepj63jt6173tDZYza1SG64wMjLC4/dPcfJQg6s3V3H3OriWptSNGSPHvIlJtKHb6ZE6wuhonWa3T4ii0fEHSkpTLuRYV6kQ9fp0uz0UkMYhYeCjVlEDtviZHxnJMoKUodmZIY3a7Nu7B9NrMZ/XdBcdGvMLSLFEd2qK8ZEq2zZuZKxeJe+C6ICp2XOcOHESPzIs9fr4ieJTX/hnXrFnC9XRlVeu5XwO2xZCnSKxkGhFkmSBSm0MlkqJLI1tG2LbEOkUwSJJFIkoUIZYp0RGo5SQ6PSpmI1gMGnWDqtZ4p1ON9NdQLfbpdts4zh5Cl6R0CtjUkFchdY5xse3cvrsCbycSxKlbN+xHd9fZOrsSbqdHr1un0Y/wU8EE4W4uQKW69BeWqCgL+c1LlfcJy5oo5FlgmzbNuVyGTCM1IZJU82//MunuPXWl/CyW1+WOfiTKFsmRTGNVpNzs9P02h0OHTrEgYMHOHfuHPv27uO8U1xW0RWHDj/O6NAErj+MXwi54dYdxDNnWHzSJpcaRjeXWFrsMfGEsGPTzdTLD+Hmitzxqpv46z/9n4RJn1q1iugWi0tzTM+36Ec53vDGV3DnnS/jfR/+DGNDw7SHV7ZwRDk4jouyXGzbI18oYNkK3+9TGxrm8ccP4YchjmdRHx5m05ZtJBoq5RpxDMoSorALxub02eO0O02G6iOIWNjK49z0NKIsKuWVlXg1n2JIsSyFEUOQ+sQCYbdHO2jjug5BoYAtMFobolStUHZmaDUmScIusSiOzR5gcWgYZedw66MoZRPGAbHWiI4H8ZDVZSSLfaT0g162dLYsbOWgxCKMAlrNLqUkourGhKUSo8M7cXNtvMVF2r5h88YtPP7wV9DdFm7qklfgKfAcC6tWJExjDj95eFUejzzyCI1Gg907t9Bpt9i5Yxu7r9rBoYMH+IF3vIP3f+D36YYRlmURBAEbNm/CsxVRFFE2gkk1grBl4yYOD03wyU99ii1bt7F3757Lnsxecus+GgtNDj0xyXwQMlwdZmpmiXytiwpiqjmbiAg/0qjEUK5UWWp3SLTBdnMUPRcsiziOcCyol8ss6YR2PyJJNdVygempKXqrBBQdKwQToyJN38sBioprqG+usW/7GAVrO+bavbS7bVrdLkdOTrB37w52bd/Gtm076bdbWFqj/R5bt2zhkSeeoONH2J5NrV4jSpo4DcUeb2U5LRdcnBginZAPIEkMsbKIUohTjfYyC9uyLJSlkCTGaIWtNLEotJUiqcGv9eDIAAAgAElEQVQygiXZdVpLNvGKQfT5wO3KSrzb6WANMqk2bd7EmeNHcQt58uUC2lMUI81Q0SHqtzl1/CidXpNHHn4Ek0AUBEycfIJ6rUwuV6Cx1CQUDz9XQktAtVAgtSyUAmc1RTbAFVXiYRTxhfs/TyGf44477gRjsGyLHbt3USyVWJiZIV8soHXKb/3ObzM0NsrS4hL5fB7XdbP8zjAi6LTptzq8+Zvfwi/8/DvZt28flmVhksur9Kte7vL2N/j0wzk++Odf5uzpxzFKs6+zmWs3hfz7PztIqhRX7c/Tbj+EFBY48OhDLJ46SXXIxapspdlqELUCrFKDZtuQpC5RZ5F7734YO1KEQY8nTx5ZkcfY+nFyeZckjbGUkHML7LlqNyhFuVLgmmv3sXffXvp+FyHi9OQpRoY34xU8Aj9iZGwcSYQk6vGVBz+B1n06nYhScYiR4Q1s2drj1KnTLC4trdwghRK2ZaHTFCVCWcBofxDVj/En7mcOCJWDP74dhz3krS6Fso92QwI/IGcC4oVZKrVRok5KFMf0fU0YA6nBmITsZYOXRpqmNOYXCHq9LGgt2QBLjWBE8P0uJ5+coGbanD08QW3ddVTzm3ByfUId0kstHps4wuOnjrK5lkP1ezT8Pq7rkvNyNBYD3NIQu3fcsCKPxfkFDjxykGK+zAd/73dwLZug28PzctTqQ0wvzvOF++/jn+/9Eq7jMT42QmdxkY0bNzI9PU0vDijbZZQoRkbrvP41t/OlBx/i5376x7jtVXfwrnf9Muoygpzv/38+hB257B1bx2g4x/56mR3bt1ItuEwfO0OYBFSVwc3FiJWnE0e0YouhagmHiHLOox8LncRQ9IROt0ukBG0rjEko5xzSJEW8lf8la2PycR6+7y+w/Yg3fs+vYbem2TxWxBvaAHFMpVBC5avU6yMEUY/Nm0axlE2tOkIYBWijSZKIbqtDa3YeD4tE2WitmAt7OHGFj52cxC1V+Z4VeGyoK4LAJgxTujoltgypOASpEKYphVCR2BapFuLYEMcJgQKdKqJU000jVJyi/JhAEvqpIUVA2WgUmMy9u6pP3LIGvnjNgQMHqI2vY/S6fdx01zeRWorhQp6wH+OHMb3br0LHhgPHnyDSimOTs0xMeMwsBmjlMzSkIdIUEhdbp3SnmiS5hHqSYl1mAPyKKvHFpXn++m/+Cozmlltupl6vAYZ6rU6+UKA2NkKxUMCxbBabDfxzAVu2bMXECc1Gg2azSa/Tob/U5j3vfQ/f8ra3YrlOlo+cplkWy2BwrIQziz6PHcwxvZRy4Ng5AtcjSQ0NO2R7Jc9d19p86XDE4QPT9E0LpxKi4yKbRjexaUuV+SDi1KlJwk4fh5hScYSN67ZRLxap14f5ru94A6EkTJ97YkUe5coQrmvR93t4+RIbynWiuEez3SDVQrFUY3R0E9PTp2g3Z4h8n3yuiFGG1MRoY1DioSXEccqUyw69Xo98rkguV6JUqGFb0xTyzoo88vkchXyeKIpQSpGkMSaxCIMAQfBsQ8FzkXyJSlFhmR6SRliWg1sZReWyjABlKdx8mTDskmLhuRXsfIHh6nq0jrJ0xBWgtSaJE5SBuB+BMbieh+UoksxbTskrQJjQ1Yr1xWGmp08w4uTxUwHbol7OYaGJtJCkBgEcpemlIb5O6KcN3FXyorNVn88N119NvV5HRzGWAdfLoQUcx2Hr5i2Y9H7QmmIhT2Opwbp1Y3ieR6/Xp1qsgs6sukq5zK4d27n2mqu5+3Of4Zd/+Vcvyxp/4uQ5pGWxvTrC7q3j7N+/hx379zN77gyphnrFxXJzGKdEtx+Tdvqsz1eRNMBzDbHWlHJFYj/GD2I6QUysFTpO0WGM7Sg81yXoByvy6J07TCXoccOOHcweuYcNORepbCB2bIqVIYxKCJM+YRhgdIItFtXyELbtZhvFdEx3aZpev8Px05M0um0SZdPrBvhRhOm6xC502iu/ViZngSHG6JA47DB57hxj4+spFEs4cYwOemiVkugUP+6xtDjL7ORZwjChPjKCJkLCBL/RoUPKQs+n1fXZsmM3heoQqGxzm6iV+ybbF5C5hDudNvV6nVtvewXrN24kigMiPyDBJg7alOyUNIkomojFuXmW5peIEsFySjR6EbZKUOEclk5wtEaRoARSy5CalVfy53FFlfjo2Ai7du/gkYce4k/+5EN8z9vfzqYt28nnc5TLZbQFaRyzMDODYChYeaZOTdBvtvG7Pfp9HyXCDdffwJve+q1ZrnQYZpsbJEt9uxzcfs0erhrxKdlLXLPV58hcQjcpcKoXcXTG8G9e7nDbtZv5+ANNpsMKvmOjkoiCq2knbUQphoeqdGyYbZyjXqyxf9d2rrl6N2Prq7xsdBPtSPByK3eCMtYgfVoQJ0eUGpSbx80nROn5Xak2rl0gCAzdzhLrNvexjY1g6DQXsawypUqJXKlOe+YcxsjAl6zRqaJSrlEorPzPw/UgYFwsl0iSBEtbWCIUi0XSJKXX6ZDL5XALOfIOoH3iqI9xPPohjI6tw7Is0jRlsdmgUCzSai0iRUFcQ62+EZ2mRKss2wFsZZEvVPnnz3yKUrlEpVRAXBsn5xEnITvHN+OQ0A5TirkSadCk27OYXmwzMjbKhpE61UKRIDLEKYgFdhhTztkEcchIrYYyK6d+FkoFRkfqlEsbcRyHRGcb1FCCsm1c22bb5s3kPY98Pk+pUESJkCQJuVyOZrNJ9v8OBBGFY1usGx1h7+5dHJ+YoNVqDVyIK/vF0xSSrqGXhLz+VTew7+U30vUTVCjUqmWMSckP1bAtwXK6WESM1oq4qkiv32FmqYUlFo7u4RsPg00YRhgtxBrEBifvIv3+iu1xzdYhrq/fSk1rJvuzODJEq7lAoTqEFUYoO8D320RRgOcUwTi4OQ8sC8dzKaZF+ksprXaDnft2M9dbpNvrIiamUrGpmmyz2tXDwyvysCXFNhE5EiYOP8Hdn7+HG6+9mptvuJak36G/OM+RI0fRWrNubJT5Myc5+KWvkPOKTOfyzC0tsqE6RNoL6FY9Dk+c49zMAtXR9Vx94y1c97JXY11GYoQ5v29isDnIdV02b95MLldA2Q7dfsrJc1McP/wEabdBe6nJA48eptHtkjo5RHTmi09h4skDFEnIFyw8sfHDkCS2sBFs6xvQEi8XS3zLW76VY0ePce8X76PRaXLj9Tfx4Je/kqXodTukcYwloOOE1vw8QRhiUoPWhu07dvDd3/123vDGt2R+KzNILBzs79FkltdqAaPRWo2xImwpKjZ++zaOnTPc8+VF7nlCOLgQ8w7b4qpbCwxd4/HwwXHmwz184pMf47ETp1HFHHiKoSGPvFtFt6fYPexz4/oeO4YStGnTbQs5p0hhlWVZEsWEfkSShjheHk8UftAiCCM8J0cun0eJxZ6r9lKrVbn/i/fS6/usr45BGhB22xRreeI0xnELaJ1Z1X7YI5crUCyWKZXKqy4PxRK0QGqyva5KqSwfHwEF+UIBBps6stSsiJGhIbpdnyQICXo9xsfHCcOA/iBTKJfP0ej3iHoRS0uLJElKuEqWjAi4tkfZqnD8yATr1o3RciAKQ8qVGnESk7PAy7vULJek36VcKLDQ7NDuhqzfAEpp8vk8M/NtglRjVELRtRitDxPpCM92iaOVlXixXObOO1/N2ckTWQZDmqIG/nnl2Hi2y/joOurVOrZnUS1XGBsby7aAG0McxyRJimNlqXkYoVTIUSkV2bxxPceOHeOWW255KvvqUkgjiHzDll1b2f/Ka6hs38CZh49StT0KQzUePTpBY7FPlGgk8tkwVKHmd9i4fpSiYwj6AUEcUskp5pcCwiiLF1UrFfzQZnhThcZCl2aztSKP6TMTJM05xh0PyVcZqdfQ2sJKIOx0EJMSBF3CsEeuVkSJIvQ7OF6eWEdE/S6SJBRth2s3bccNAsQy2ZY1S7CM5vDh45TaK7eH0hHEPjrwmTx4kP7ZsxxpzqFPHcaRhLbf4dSpU4yOjtGNGwRzpxj3YhwnJAp9Cn6DsNsiDWOWOoL4IUFzlqX5WcoFj5e+8o7BqwRWpPFVS/y8vBSL5HI5+n6AH8VMLXR54NGj3Pv5L5J0loiDiF7s4Oar5AseJu5jcACDBbj5KqlbohV3iZOU4TRLBlhlQfAUruyOzdSwd/d+XvbS27j/K/dy/OQJjh45TLfdIUkSbMuiUi4BQhKEzE93sIyiUK9yy60v5Xu+5+3ceMONiFhPD5INvmvJDp7arn8JfOJLxyjsL7EjF7Fh6zDF6jk6IyFfUA7HG1268RiJD9dft57F6ZjgXINNw0uEvQjVK1IsWgxVDEN12HdnnR0jVcbcL6POHSVy9tHp3Ipy8+xNmyvyyHsKbSz6fU2hkMcYULg0dIpnKUqeQ2txjjOnDrNx03buvOst/P0//C1b5kdRJkQlmo2Og+UoyvkC9WoVUYZGY4k0TQBNq7WUKeEVYDkOlmNnOzZtCzAYnb1rwhhDoVDIfIbKRSO4OQ8vXyaJDXlXE/ktZifbuJ6Hawy9XooqFXBslyRV9Lo+cRzRX8XiEyUgipnpWYyA3/PJFRyIE0wU02u1CCSmowyJUVheAUtXSCSi02wR+yNZBpNjU6qUscOEpcYchfIQru3geNmGlXSVHawK4eXfdBvHaiXigavOsi0czxukfnqUi0Xq1WwVtHvHTgqFIgZNEkf4/T5ap6AsUILRYFsWoyPDDFUrnD1ziltuvnnVLKqkq9FaKA55eMMFgl6bSj5HZ2qOfNqnEST8nwPHKNXHCRdnue3anXhxysiIUMyXqRZDgqUFHNemYGtMogmThDTo4VqG0kiNXKFKt9lZkUfUmEbCHqlU6QUBbrYLj/biIl6pTESKMSFBt0XsVfAcQ9qPseOIJE3oLM6ig4Batc6TE6e454FH6EURynWwJKVWdOnGMWZx5ZWailpMHniAwwcPYGZOcd1IgfbiHNG5AOMIomMqaYjuNli3bxvjw1u5eeM4R6bnmemFlDyPyROTuJUK7eYShXyFWqVC2GgS+S36cyfQ2iFfqq3IY3mW3fnVl21bNFstWt2Q+XbIsbOLnGuGOImNLS5pwUa7kBhDGic4lkViIPTK9C1FJ4A4ddChoiQxtmSJ1ZeDK6rELctCa0Or2WRhZjb7TVkopSiVSvR7fRYXF/H9gKAfkHML/O3H/pb1GzYiysIYTZoKSn3tlvvzEBHSVQJoLbby2SdnOJO2GDs6xbX7Kgytc2jTpq893vD705TzM/zQW3by5jvGedPtM7zzTTtR/Tkk1JDbBOURQlFw6h4k8tHaRuUWaPsLfPQTj9GQXeAVefMKPO6/72NonZAveFQKdUCIk5Cc65B22yx1zuG4DidPnODDf/GX5MtjONLnzKkJPMdhdKSOKKiWz5LqmGLOgFK4o8PYliIMUrZtXrdqnnixXM4CiRjcwVrSdUv4UUKv10NJjOe4iKMxacLS7AIjw1uobhhFvCK5XJlyqU4cpxw/eQy0hZWrYwd9CqZN5HeI4mwSXwmWZeHkczx2+hAbNo7Ta/WwlUeuoGg1ZnFcF3RKkkbYXglNSqw0+bxi87pR6vky8/MLtFOfsQ1j9Be7jJY2Y9KYkdFRonaDXuRjed6KPACKlRq5Yo04jnE8Nwt4OYMXOimhWCqwbfM6dm7fwTe/8fXYrosyCUcmJ/j7f/oUb/3Wt7Jn9y7cQgFxbDCGjePr2b15C/W8zdSZCTZs2b4ihxG3ytj+GluvqzF58ARqLmAo1US9Bj4xe3dtZynxOH1miltv3MXO8XWcXfRpHJ1i+1CBumcxMjbOzMISwyUoeAl+rACbQAvN+SVac0vY6cry8bkvPM4tV2/j2qs2M3nkJGemJ0gTi0cfP0GpOsRr73olDhG9pE93cRpTqNHt+JTzeRpz0+RzOQqVKqFOOX3mEOtHLOr1DdTrdYzt4FVqdCPNw597dEUehw4e4xP/8I+MDRWwahrH89ix8ypEJwwPD9Np99juKIbXDVEezt4jM9frk7oeD37mfg49/iRBlBAkCREWlnEH71kRDh15gkO/+gts2ryHH/jhn1qRh+Pkntp9vn/7ZrZv3c5SK2K+MYcfR0zNden3fXL5AoGfYJSDlfaRqE/RzVNwFY1OiFaK8sZtxHqRzrlT6LiHbQldyyGX+pQuc/filU0xJNu15zouOhlsB1eZj6nT7tBdapLEMUop8m6O9Zs2sWXbNqIo2zBy3oVy/p0HF+IpN8oqy5Btm7bj9WDuxAks22FJF/FqeWx7CT+1scTjmi1VRMa4+/4eN3sOW4a7zM8UmDppY/Ih+ZEmQ0WbdQD2EoYaqVWnWol5080NPn3qCIdmVkmZKnq0OwFpDGmSpdQVPBeDxmCwbQuIGBmuU6uWcfN51o2MYrTC74cstXpMzS1RLORQRIT9Dr0owrE9LCvAmJBSMYfnruybd2wX21IDP1381O68Qj6fvTwo8XFdD7ELpNoQBBFnz55iZP1miIWOnxLGEMUxk2cnKFaGqVkVXHKIdBmvL4KliFcJbAI4nkuuWMARIU5SelFIvewgovE8G7QQReB5eVKxcEp5yEXktEcURrS7bbxiHuUKtiNIIuRLVeYai5SHK8ydW8DLrxwjgEFu+vkUR8t6ymhQkrnvSqUSG8bXUSkXqZQK2fthgKnZeR46eIhSpU4YBlx33XW4jo0oheM4bNy0iUqlTNDvkcYRlnPpCWXb7s3c9tJ9WPmEKMrzxfsfYtSz2LyuTKXgsLPg8JabdhHduINCTjE31wHbwS54nJqZpe1CqVim3w+IEogHO0Y9N0+v4/PA3Qe4btcuRiorr9TyeYdaySJXEGamptm+oUq5uoEg0dz/mXvZvmUT2zaN4agihw49zqbNWygUXRpz/x977x1raX7e931+5a2n3zK3TG87u8PtS1JssmgVy6aiQolGlEApRpQEAWzAgIXYSZAAAYwgiIP84QS2lQI7zU6MBEQCWaQKKYlLernL5ZK73DKc2TL19ntPP2/7lfzxnju7lDX3boKAooB5/jkz99xz7g9ved7n9zzfsoe3koPhiG7lCeOAN25uMTMZwXBAcOcms0mBUYqFtMve4Oid2suvfp/9Ucbu3gZelYRBwFOPX0UpTW+hx3iUsXX3LrPJHq3dBt5E/A9/8FVe+vb3GRxMKamp7DW1x9U7ISEQzqG0QhDw7EefY/GY3ryUaq6MaPjJn/kc5x75CElricFwQq/XZFrC+dPrpJHk+ttvMhnPcLmhchnlbEIoI0pShDcEvkIKCxicNTgt6M9mLEcOL38EB5t4j1SKK1eu8Pu/+1vgLbPhDO9rRIkDojQlSRKk0pxYXaasDPPm9/3X94twUf/7vgxZ/XSUx0FzvGWvAEZwYBOGr2csnwg5nwZIq7AiRGvHH7z4HkpKbvd3+Y3/4AS2irh2fcSb3xpSlhlPnpb8qx9zCCeQUY7THoTlUx+LWX7yBM+/cfRJKMtaeVFKTVWZWmhIiFrgaTarW0xa02m1WWo3GU4LpFhgdf0ki4vL3Lr1NnfvvYfUe1xYSVBSoLXE2AKpwFOSFznGHNM+kKrGq+PJpgbmMqN1AtNIO2fYKkGUJIggRAjJwe42OhxQlJZBGDOdjmkEBuf7jMYBoUqIwwGXzgYsrV7EcfRD7bAfHyUxkdIEYchgNiXSEXEYoIRARzFV5dE6xBrPzJVMB7s1AUQItNKkzQYWC9KT5RlJI2VaZCSqc384eVwIAWvra0wOtucCUPX21lMr30VxxKVLF1hdPkEShcj5dbm9s8fdrV3euvE2ly6c5eqjj4K1aF0Tj1ZXV7E4TJFhTXVkEr9w9TSXnlznOy+/xR++cJ2XX3iNZqz5+FNX+MTVi0TeEFUTWgtt4mbK/tYBUVCfz4mp8fGqsgynGYNxhtSaJE4YTA/wQlFlDsp6JnJUrC1ENGOP8TN0IIhlQCuJ6HYT0pbiy1/9Csu9ZRYWumxt3cNY+POf/RQvvvgSd25v0Gw2aDYTZkXBcJLjtcdikGGAVAFOQV6WzMzRD/misjz1zHPcevc1iuEOqY7YuPEeZTYj3xuwu7lHKgzONRiNRrz55iYvP/8dqgIaXqNEPVurH7geKysO5ToCL4mbbVqtFu+89+6R66iqWsY2iiLaKxewusPuYMqgv89k5HnlO2/w6mvvkhczqmyKK0u8SOgsrnF6fZnNO1tUpcdXM4QSDAYH5EXGof6qwVEJT36M6udh/NAZm1IKms0mZWWoqowoSCjyWshmcWUZ5xyz2Yx8MuEj8z4xc8nIWlHvA1/o5yy+Q1GtObbYH5fEg4CxarCxb5ndm7J8u+RTlwo+vbbESRFyZ99ixmOQy1x44hK3dt+m2hOsXAr4ydOPwZdnfOOre/zhW30+/6mANIhwgcaHBT4MCZaaPBZFeHn0tj1NWoRBhMcihalZZMbcV4fL5tK9Qay5dHaNWaHYG0/Z3Nxke3uX8fiA3sIyWWkprKhVEEWtCzGZjJlMR+RZRVUdvTUJw7qnh7MkcYpz5j6EysylZlsBRA1JkiaEhQDdo7IG63O0cATSsrzYwoqCTETs7O1R5iWdVs6tOzHjbInKhJw8QntK+HrYFTVDpFCEQcBwNGRrUNBKQpqhrrVBrCJEQWQo5QynLM1mAtKigFiH2LwkTpt4B5W0eOXJshlRWPf/P0ykaYPJweEOT9SIEyewzhHHEVcuX+bU+jqBrqnfxlmG4wmV9dy+d4/t3V2qsiJM9f2dYxzHjKcjRpMpk/GQhaT5wL8/mIy4fe8ug90RW5t7RM0m/VnO1159m9sbe6x9/ie4uNrBWkc2yQi1RlUHWCKcFxRlhTE13NMIhfJiDicUoDy/8LnPwCxj4+BoGYKVpRShPaPJiNMnV7hw9iJhs0m75Tl/rsG0EHzju9dIQsnaUptHdF2pzmZTNrduc/HiBYxRnDt/npeuv04ch8ShQnlH2gjR0pEPMvLy6CT+8Y89w/dfeYH1tXUm3nCi2yXUkjKbstDtMbyzwWKvTafRZGN/h93+AVcWF8lKxbSQzDA4V6G8I1BQ+VoxMxC1wF7v9DlcadjYOvp4HDLNFxcXuf7eBje/+grthQbSz+g2Qr72/Dfp90ucKUAalAgIGi1OnTnHT372U/zB7/0Rt27fwzqoqpyiyHCuohYMra8T5/yxbeHD+OFW4vNBwCc/+Wk6vQWKMuOnPvvTCC/5yle/ghCS6XSM954kjllbW0d8oPI+vBHuY8F9XYnf74//CfK0f1I8/+LLHExHzIYV62lITwjCScbFUymnU8l3VczewNM+eZbCGYwJ2Hl5i4XC0Lsg+Nm/+CjLaz2+/MXX2azGXFhP8Eoimm1k7yylOMHmjX1efv4Gz/5rD17HoD/BeUOz1SAIZS3iU9bsVKUUaZKiAkVppqwtN0C3SIZdtjZ26Q8GzGZDvLB0223eeneTxV6TNFYYY5lMZhRFBSjwx+hFu8NjXI/mAx3OUdn1MRVSEoQhKtBY79FRQhg2aXU7jIspsyxHKU0Qag6Gu/SHGdgIpTSlgZs3S66/+30sIT9xxJBAIZiUBSqEvKiYjMdkRc7+OCcMNL2GoawqwiAk6XVYO7fCiH1UntZD1VCTzyDUGqcFUa9Ds9thlo3QMiDLciId35dUPTK8xEv9A4QLZx1a14gdj6fTbNFtdxgOhjjviNIGyBpnvH9wwLUbN7DO4L1DCIm1dq6Zrtjf3mJ1/SQLJ04+cAkvvfA9ynGftIrQ0rKy0qW8u0t/knEwzvnN33qBTz9+jqvnT7C+eIIoTvj4R85x7Z17bE8nrK506A8m7PRHlE6CMTxy5jSdTpu7e9uElGSmIDtGmtdJx3iagxsQuYjd0YimciiV0wgL0G1Es0llM1Qc4CmYTmrDh6XlNguLLZ579sewSCJlaCcxGkOIJ40DNJZmJ2Z4MDlyHVpWTIZ9IinJ8pyd/V2Wel2SNKXVabN+ZpVmkrC0fgJ9cpWss8T0S8+z1S/JQ0UsJVoENLRiOY0ItCLSAYGQKAfq9HmaK2vYztGDTTcf+Dvn2Li3yc13bhNvWJSc8vjVK8wmY7AShcFWeV1ceMN4sM9kOGCh0+SO8hS2pCxzrDO1PIKvdcRDIdHOEQdH8zvuH5cP9Vv/P8Uh/C8IAqQOaScxP/UzP8vZ0+f4iz/3L/G3/tbfIEkSkiTBOksYBj9YeNcZe15p+7mTzwfen2tRHwfdunPvHkVVQtRizxXsSMlEaxo651y3RJQ5b1Yh6WKEb3SIrOfW63dp5gfYOxXNdcOnL6zQ/vxZGl2NWATnHaVsMdmP+M7vXOO1V97j229n/DtHrGM2nWBdBRhajR5KaQSSqipRWhLHMWVVILAIcorcEAUJvYUmaRphqhiJQ1Ci05TReIapFFIKgqAxFwMLsOHRN2kcxrXJhK3mcE0FSJxxIAMWVs6ioxDvJZVPCOIms7xPWAqUjqiMY5pllJVlPDMY54mjhKL0lHlG0Fwlq0K8Ov6irMoC5T2TfMba+VPsXBuxsbVDM0nRUhHHITKSnLl8gUeeusju+B6TyRjnPGU2YzYtwIGpChpxRFk6isLQ6bbwvqKddEjS400hhJC4eZFwqD5YK97VbSWpJFVZUZYVQRiyu7PDuUuXqYxBybrtctAf1IYb3tVeHdYSBAFRGGGtYTabHrmGxe4i3XSF3Xdv8bFnH+X69feoFhokrQa39od8/c1bvHNvm0urXVZaTS6fW+PyuQXKYkYSCOIgopla3PYu/YMJF8+dZWmxx/7eAaYq2dnqo8MW93aPRoXkRTZ38rEoC3fubLBU9XBlSaI106yuZhe6EauLGummVPkQ5yacPLXKM09/lOWFBX7vq79DK1X0Uon0IIwjVjVJS+sQf+ro5Kmlx1pDVlTMSkNpSoy3tJsJQaJIWikozcLJNZ64epmPAqN3b/P2117h3sSQO0MAxEowjtdcBjEAACAASURBVCOaWpHokFAqIqGIOhP8ZErU6hx9cUiFs4bKGl5/9XW8CRge7KJ1zuzsebTUGOmRDjgsOE3JcG+f3/3SlzCVB1cyHu0xHjexpkB4B87jnKXd7BAWozlC5fj4oZtCOOeoTIl3hr/yb/67PP3001SV47Fej4XuAnfv3OZgMqXV6fDpT/94DZXz9fbC+xoHfujK8ccnmN4dWrkdXXlKKedoixBDizcrwXduFvDujHY44699aoWPXDnBrBtQRE0cgj98ISftQxxsEyUDVM+wHmt+5f+ImY5Lnmt5nk4rsIZHLjd5rpngzxw9MLp48STT2ZgwrPWaR8MJSgU0GgmT6ZCyLImjmEClOF+SBgqbzahUibE5j51foapGZGVGVYCxMYXxBEFMlhXs7u8RJzFpcvQ6JvmQqrKUhSFNW4RI4lCjolo8K272aCURo8EBw+E+vRMRjaVT3NrcYTrbI202cU6SVRU6ahFKjQoihpMBo37G5dM52ntkcDS9ezjsM5tOuPnmW3zqZz7D1U88xy9aQ393nzAIeOFLX+Gda9f4+I9/nCc+/jRhs0FnbeX+54UH72y9TZ7vyrx39Rt4jHVooYjC4webNbQy4bWtDc6unyKUiqIocGmMknV/e3l5mTAMSdOUb730TdbPnKXVamGModfr0mw2eemlF3n8sau02h3KsqTT6SBkveu68f3v8+jTn3jgGpabmq4KOPfcc3z7268SlDkXVhJ2QseZH7tItT3lnWvbfPP2HlW5gf7eDX7hqSdhOqC3mHD97hZFbmgmEecuPkKzqcnJmAWSnVxyaX2Bd9+9x6kTRz/UrOkimi2MVIwmu6gEpGhiypwwiHh8fYVzKz18OWCpG6G05e0bb5DnA3qLV9BByP/+T/8n+v3bXFg5cZ9MU8vGQqAEsVJ0GkfPkPpTWDp/leHBHpuvvsHoYBdBSbsREseKEE0gBZtb2zy9c49Gu8W5C4ucuLXEzXf2yWaeiXM447hbTPHSIpAor1Be0Zy9yuONNuvhg1tcAFJYmmnAF37556j0GndubXDQ38HYgt7iOsuLW1RlgSmmTIuA0oLwJa6UDEY5QRxhqwzhhmxu7mGrbM4iru3dksUTiKmiqI5msB7GDzWJH9Kuy6Lg9KlTPPP0M1jratkq77l0+RI7ezvoMKDbW6Db7QFuXhHNb0qAB7RM6i3O8U+vQ5EcAOVypDKIoI2hw6jK+G+/skHa2OOpT3ZZXksQUrIxHjMsNbkVqLI+Ie/th7y0oWn5KX/5quZjSzHDTMLlRb74zREvvjPj3z9iHdvbO5RlRhAq4jhCqYAgUIxGI/I8Z2m5RZbleAwIA1iwkul4QCNpoASUXiJ1TDkbo1WAn1XEcUraamHdOmVZotTRp9l5RZKmtNsheChG+0wnBSoIqSzs7/VZPLGKkCGjfEq23actO/ggpbfUm7dNSgoHQmqk0ISxIk4T8mkbqcGRgzs6eU4nY6qywFcV6+dOYbBIHCcWezgBzhR00ibttE2WTXFaEDeaHA4dER4hFeH9Z7h//0XA4YTiT/bE/ME4nAlMZlOMNYQIqqJERsF9xl7aSO9byjUa9TDt9KnTrK+t8exzz/LoxfP0D/rsHxyQFyVxHFMUBSqoRd8m+dGko8QrlNcUuSPLKlrNJnEsUV1FJTzxygqXOqcYDGa0lk/w+ls3uLnTJ5WOrm6w1mwzHE2ojMGbKdLGiCJAAXv9Ay6cP8mJ5SVMdTTE8OJSlzAJMVVOpxXTiDWyKmgGAanSrHcTVi6f4o3Xvosynsk0o9nscGblJCcXlrDTMRQzWpGgGUgqIxiOx3hhaUQpo1GfUEcst5aOXMfOKGdsFbmMKaM2EzUlDhqMBYzykiqfoEuD9Z4gjgm0YGNjH42m12yQW0tZOZyDyjisqO0G3bxinmQjdvZ26KyePXIdj5w7y9r6CqvLC6ydv8QTVy+RmYyqLEmDFk9cfaR2x6pypkVBYTxV4ZFSIcOAwWTMK9/854yHCdaWQG3vV0vjGnSaEEeLpB9Sev5PxZ5NSMWv/dq/Qbe7gLUOKerK5PEnn+T5b3wdqRTNVrOmfVsHyAfCCv+4z+aHicMb1DmHIkT5kFJIUJ62CBi7LlvTkun33uPz568yHQ24tgdvtkPOLjc5IQ2xCphY+PxazmKgeXa1ie+E3NqP+Uf/+B53ZxGkR28P9/Z2aTQSbF6hVH18GmmDNE2J45SqNFSVZZaNCCOJd5Lt3RF7gwHNZk6iJL2lRUxVUrkx4AiUIAo0oNBa4Zwiy46GbkkZYYyjMhmmKhkOpzjridOI0gn2BlMmqmBhsY1qBQgpmeWGMG6go7hGAzlfk1uUBBzIilanhSYgjDKS1FAdgxOvqhIJtBoNgiTAYzF5Rp4XVALiQLPcWSRSYe3DGh+iXeqs7RF4wftMNy84NOq979on/Nyi4ug4FKeKwoTRcETSW6AocrStdxOVsURpAy9q8pC1nv2dfVpxyi/+pc/xc7/0i6RJzP/9T/5niiwnTVJGwyECWD99knZvkeHmzpFryMYVN67f5sTJ0/RWl7DjEcZmrK+dxlcOU0YsNzRnnz1LY32VJx+7wt/9e79JKCW9hRbPXnqC3lKX25sbnFpuMBv0wTpCIVhoRYyswVZQDo9up3z6yUtU3pJNpzRUhGx3GM8mxGfO0u0tI6QiCjxnV08SRU36mSOfDHjiyaeZ5Ja3rr3Kk5cfodEoORgVDMclWgToEBaaDRrxGiGe8TED+M2DCZNJTp4b8rCBTXu4OKD0lrKYMawKmBaY7RFW3UGUOePZhElu0EoSagXe1W1Y5/BC4gR4IUEpHI6DQZ/h6Oje/NmLFzGm4MVvvUzj2nUWTpzDKslsOiWW0ZwP42g062Jqud3DVZDlOTIKMJQsLi6xfS9hNjM4X+Gsq7sNztUP/nu3aITH8xngTyGJe+dQUvH4R57AGo8QDmQN9Dl/8QKtboeqKllcXiIIglrYShwO3/5Y++SDdm+APaYX/sHPHYYRIV4qYlch/BQLEHdoRp7ZrOK3f/cPmEzH9GeaL97IaW0VnE0kJ5cUcZxyvusYe8H/+L2ce5MR98ZgqogoiXAcPV1eWlpCKUFZ5RRlRqA9eZGRJs25FokjjiWVmVGVOc7B1m4fEWqsEIxmBf4gwwoQImI6myC9Y2+wj5IBeTGr9dqLoxl50laMpzPG04zKSdBNgkaTQgcY60h7HXTSZnt/hDMVUngqu8/SyhpnzpxDaIWyc2NcwFHhXQQGtABTKdKwJCuPrvicdbjCEIa1Rri1hsl4RJnlWKDRSBAqxRuLzQ3iAc6V/tCm74EopQ/JZwaWV05iZjOcUFTOUlVVfW6MQQchVVUxnc5YP3WK7e1dTGX4/C/8Io985CM475AqYG9/n9XVVaQQTCcTgkBjnefUmaMrvt2dMZPpAS+9fpPP/PSjnFu9wKmVNYY9y8a9myBimFjGZsyN72+yt3/Av/xzP8Wte1tMxhOef+sNsJLJpODf+9c/R6vbZmt3j2IyZuFUg5EYcP70Oq55dPvgnY0d7uxt0kpiznYX6S0t0A5apEqQhpKZd1gLnVabJEpYPdWltB1azS5iaDmxbrh8fp1YlSwOZiDrwXlVToljRRK2UTbnGPl9tveHTGYj8nJGJgNM3CSXEm8MJTCTDZwUUFnM3hgzGLM3GlCpgMzVszSl652UsrU6psMjtEaGMVpDZTyD0dH3y8ZOn7yYcu78aS6eO4dTHfYmY7a3t1lud8myDKkk794ckjRarJ8+x9bmNmVVETdTbt+7S6+xSBSlTCYDrKsQrsatC+lJg7gmAf0oGiVb5+6LVHlvkVLdF2sCweraGssnTnD71i0GgyG//dtf5vO/9Eu139wHbJTug1E+sF2ukYi1OPxxRCfv3/+sEbWhqaKsvSR9iLS1IwgCdvcOyK3Bpy3eNSWTseXlvifYmiHkhMzUjjilgEAKpIpodyIE8thcEScJVVUipEZ4g/WW8XSIdRYpQ9IkxSMJwpjxZEIQRqyu9kBL8A4dWLJ8iFQBkQ4QcUyWjRmP93HWM80tSini+Og2xtbOPrO8YppX6KSHkiF5ZqmqHOss7VabIi9Qcxd1EATS1Q7mUtbbRKXxXqCVQilFpGOELNnYuQnWcPniCv6YStxaSz6eIpSmzDKCSJOXZX08EIRxTNpMKcoSM5W0jnpmH/IK+OBr/e8PU4lDfT2dPHOeO++9y3Ca41DMZhnNZhOtdS1/7D3T6ZTVtTUms5z2QsTSygmkFHjrETpgrz9gOB4TaE1ZlkynU5RSXLh06ci/vz/q0426zCY5ZeYY6hlJMuTG1i3aJ1KyQZ/NzHEgSobVhFk24rlTT9HtNbkz6jO4e5fTCz0eOd9iOskwVc4knxLEARfOrvGll18jSCRtjq74dBISJhEEitxbYq1oRg0qaquyUCusTEiiRo37BsrSce3dd1hdP0ejE3Ew6ROqOnm2Yk0UBxirqVxRy8F6QXxM5bm7t8ckn5CbjHFZUXlBVli8cVQVTGyIF57SFeS5xWSSnUwiI0HlLE5oZKBqAlsAsjK1jrgKCOKYOEyQQczsGIPiX/6lz1GZgrIskM4xGe9RTiZ0Y0U3kTR0SJykBNITxAmdZojrNpBKkbbbTAd9djY2UQRIynqjOCeXIeCZxx/lp37iz2GqH0GceGXs/USshLyPiTxsb3SaHf76X/3rbG9v8+wzzxGGyX3d3sPfg3kSdoe8xvcra+c+HMTwgzhy4f18+x3cd2oRcv4E9CCFJNUhQgYgGhyOgKx3GDyp9yQcbtrri9HPserHKJ5y684WcRzXjuE6RClASqZ5QVVNOBge1CwuZ5GyhqgtL3frnnlWIKIAHQisLdjZGaECzWgyJgwj0maDrByQNpJjj8lQ9YgWU5aTBtYJ8jzHGkORFYCgu7hMo9GoiQhVdR/i2W53iJMEpTWDwYBr167hrJmTjjRrq6uIMOT1G/e4eecef+7HP3LkOjqtDoENyJhSjHPSbo/FlRWqskI4Tz6a0UzahHECQU08+rAttB+ID/mZPM/ZvneL8f4OBztbtNstqjmTttfr4al7473eAsPxgN7yEl5IhpMRr/3etynzjCtXn+DG999ip7/P8vIyha9Ne513hMdAyD76iadxexMWUo0ZV8zikjd3bzEzYx47e4k/+t3nOfnIeYZSMBkMSbTjO8PrRJnnVJjyyOULrPZaKGeZTccUpeH2yPDa/j1O5RXnwlV0KdgbH63xc77X4HT7AsPxFKNi7m1nvHbtBs9/8xVOn13nypklVk+e5f/86te5fuceP/7cM2zf2+fs+TP85atneO17X+ObL7+OlAGfuXKOxFsGB/ucuniSC+snGJkZeVWwNdjj00es4/r1a+RlTmlLxtmsNjs29aDSGoMzBdZZoEJgoVJkqgXWAQFS1lhaD8ikgVQ5AaI2ZQlTVNIDGTIdHS0I9vTjj6C1JtCa/+w//zu88MILWGt55qmn+dy/9es00pSyMsxKw9e+8XX++7//X6MBayxZ5XAo1tfOMhwMamSKVCgV3L+Wv/LVr/DVOeT657/wS0euBf4UBps1wkQg1aFh6WFi9pgKTp08w5nT57C2xkzXCf797zh0iKmZc4ev/v57HyrmJ/Lwuw/1yN4XtvG1GNMHQ/j7DxsA6T1qfhP7+eBMSoXzUHlbr+uY7o4OGxz0R2it6fbaWOcQKqi/S9dJW2pNIDzBHNtsKkEYNnE+oKg8xRyOeDAcc+LECRABs6wWv7LOURTlsZ6BQdokjBPCKMFZUDpAqdpNSClVU9CVrnvfQqKUpCwrpNZIKdFzGdrxaERRlAhfJzohBJcvXWJx+QTeGHZ3jz4/7YVFoqhJq1Ohgtq7s4ZLRuA9569cQQqFFxIvHEKLf/E8fYj4sGbFQgiG+wOSuIFSMUIGKBxRGIEXTKcz7ty5g5CCMEoYTsZ4pYm7Xc5cuIQ3lrKCc+fPU+YTxuMRUdrg7k6/PibHGFiX2YxLF8+wEymKrCQtLXsHe+TC8OzjH+WL/+C3OLFecWt3l6VYUgYl09RzqrnI5XSB5vIC4+EBvigYzGYoH2Byh60E967d4/Ljj9EfTjDH4MRLYSmqgrQVkcuI3//6q0wGY379C5/lzNoCdzYPePHb3+YvPHOZf+UvfBypJP9kd58bd97h7v5T6KTFcDJlqdPkk8+cJ5CWIl8lK0scnq9967t8840b3B1a/ubfffA6BsPBfRPkrJhRVuX9nOKsQVRZXfB5X7fSvMTfb7HWQN33kWsCKzUIhVAhoCmsQ2EQx1TiiwtLOFerVXY6C0ihQSkWl06g55LSg9GEF7/1Lf7Zl36bSZ6hcTjrsFLjUezublKWxbwFKQCFFIc5SM93tz+CtPv3e9ge7+UP/Lyu7uR9XO4Hk/sPJvEf+MYf6Iu//z1HJ4sfxJ6//zkx11KQsv6/O0TEOIfkX/xeOZeNcrj77ZNDhIynxo4fFd3eClHcptftobWiMkWN1zYFURiQ5wVaSYSryLMcpRTD6QSHRymNsxbjHFGoCMMWWeEwFrLcUlUFYRBjKk9wHGlA1C2Rqqwoi4JOdwFjHdYapFQ1wiXPUbpmHgoZ1vIAQYDSGk+tJXLx0mWcc2TTGf3+AcZadBiQRk0CbTHF0QNWpxUqTVBJisdgXI1MOuyXSSVx811O3YWztXHv/4uYUw2ODSFqtcJHrjxOlmcsrJxkPBowHOzh0FQWdvb3mZUVaZKwvHaSZSkRWqHjEJxEzQfyUoCWgul0zGg8QKiEtbV1ouRoyOXgYI+DJGLpRIfBnQOCyjLa3OPcE5fY2LjH6dNniJUinTlGWUG0GJAGMXnl2Ng/4GSrSWUcwkMlBHlZUGQTzsQNHjl/CmtLdDsh7hwNMdy+u0dlK1qdDpmd8MRji6x0z7PcTAllxcn1Fj+/+hSJkrSaC7x6fZOfePwKT3zkJN9/81WyeyP+yk99hssXVug2PNZZkjig42Kktfz5p9c5t95m55im+HA4wphaVbK0Bc5ZjDHzRO6gzH/gHhVCvV9kMS/ODnfvh76989+11uHLEq9drUB5RBjjybKMbJZx/uwFoiilLAuqyvHq62+wubnJtWvXuHnr5v3+OM6AqM2mETDLJzW8UYbz3FObNNcYAQ1e4uyHu7bF/6ft6MN4GA/jYTyMH4n4kEjEh/EwHsbDeBg/ivEwiT+Mh/EwHsaf4XiYxB/Gw3gYD+PPcDxM4g/jYTyMh/FnOH6o6JR/+vubXqDxTpBX9YQ61gCWWWXJS48TNQKinUb0GiFaV0hk7ezjJR6Pk4Kq8ljj8V7glKbykntDw7iwWOv4j77QfeBo9zf/9n/oC5szzgp02MGaHOELFrodYt1kdzomMJCIgKoomWQ5Vy+eQzULClGxvZ/RP8jRCBoLi6AUg8mYRtJkf3+PdzfeprPQJm0v8nf+0//qgev47H9yyZeZpawUurdIoB2hsWgjGcwMr+9s0Gg2WOt1sU6xPRgw2Nyh12qwGIa4qqDIM4QIEMkie9MZs1mBKGtmbHddEIYp3ids/sPrD1zHeJR5Yw3O1mp9h5IEH4Ti1YghEJI5Zr1G3tSiY+/P/Gsyl4Q58cq5OQTM12s6eWrtgev46Kef81mWobSmtxLy3Meu4LE0exGtdofNuxNuv7NBEFoef+Ii1jom0wFKe4JAsbq2ijUO7TRJqNjYPODGu3u8+94ugVZks4KqMoRhwDe+/OID1/Fv5zOvCKmUxFEr90nj0cYiy5K4KGhJRScICZXCVIZhVmCsReJpKI2yHiGBVlTD3EpPZh0uFDyiA4bZmGE24zcevfzAdfza5z/lFxZSBIYoSKmcu09m6/V6PPOxj2KsAZejVU6R7zIrDsDVNHRTZHiquQHDJdLmSSqnGPdHjAdThrnBKUsUR/wXf/t/feA6uuc/4Y2vCJXAK0G8kNA+v04URcwmY7LJlFAqjAYnJMJ7Zlv3cFVtemJMgfWGKGnQareZlkNMNkQREaketnBUboopCqY3bjxwHf/lf/fP/ZyDRxy9bwBeu/TU8h1CitqlR0jAo4T4AQSbdQ7hIHQBTgiccHgP1lmKqqoNO5zjb/z6cw9cx1/9+1/2uTH4uTE7h6Y2878jqrk/rSk4mURsz0qCpIXPRuxff4XVp36cWVlBaSiLGda6Gh0zF2w75LsoD1/8b/7asRCVH2oSbwYhSmm0UhgBWteau96BUo5Q1hZezkEzPGQFgpq7zEhqmqxxDqRDB3PHmbAkNx6574lchDvGFOLmzbc5e+E0kQFjM7zNuHXrOrtxzBOPPU0SS3pxk2owYzIZMhtPGO4vERpLISpG+xOKWcniag/nZrQ7PTqtHlLFNGJBI3Xc3bjD2dNHa6eMcxgMDc4ZzqZbRAbGWcSsTMlyMFNPZmaMpUBLha4MWsYUuWBjnFHMCkxZob1Buk0K4/AWhAIXChZ6npWORsijadVKS4TUeEXNNPxjgKVD2KaQAu/rpHyIWz9kvr4PC60JT9ZaxBwe6nFIJ+EY2Kv3njiOUVojlWd1dZ1Wq8ko3yTPx4Sh5PyFs/T7G4Cn0UhJG4qyymi2Urq9Bd698R7SamzSYzaFNE3odAPCUFAWmjKXFMd4jipCikBT4UmtJXJ15qgigUsT3CzBGovxDpFXyKKgaSZ4B0GQIELJzBry0hCPHal0aK9I4pSZ8syMofBgjuEWL7cCbDVFaIV1liybIqWk0Wgym014682X6PZatBoJYVBRlENMeYASHo1FBw7QOKlwegBekYSLtE6voM81yQtDUU0x5ujjYVQNI3V4cikphwX+9h5Lp1ZI0pTcVBSVoSxq8SnpPaUVqLD2JPVFgBAaowRZUVEVnjRe5JFLj9II2jhjeOm7LxIcY+hdliXKC5QQVCJHSTXHCNYieiiJFBJrLFLUNmxCig+QAD2WOolXvk7ebs4Wr+3WSqx3+OP6E6q+xsV92LHA+9r4wDtHLdMoAVnLAGiF0wICiWx3qXQI1uK1xFcBCIuQttYf8hpsBfMH9oeJH2oS12EDpQRSQqw8AouSEukVcazwPsA5MX/C1WQaLyRKCtT8ZxKQSqCFqg8iHuMrAjzaVcQSSi+AB1PN333nGmFsaHcXwRnSBJpNRT7pc+fOdeLeItneNmaYM9wbkucl06XTjIsplTCEMkZEgjs3v8/CiSXK4gAVBOiwiXeChvT0tCTIjtaLfvLMCu7kmOm0z8bBkI1xzM5UYL2AwmBnhtxIBnpGO01pRSmnzvdACiZZwWSSMxyNmW2O8bdyCAQkAt+VRO2QuKFxsaehj8HNuxrbLg+rFmpClps7n9xX/fU1lt7O7casteCZJ3c/x9fK+rPuEJ8rMHPbLXFM0tJaE0URQRjQXZDcvn2HRx99jIXeErsHuyRpyGA6ZjwZEkancd5gjEcQgNcc7A+wVmBLSykNZVnQaAZcvLTGwcE+VTWmKBzh0fBsxtZSKE9iHOK9m0z39wkaCawu43pdBjomV5JKQkPA6O4mt//od4iE4uS5K7TPn4HFDkpK8s093vzut5Be8tjHPwlLXbbzGVZClByNzxY2Q0iLd0H9oPUObx3OlCAlg/0JVTElazRotcP6+WmquWqkwVmPIAQRgM+YjjeRMqe92GbpxCqBiJhNh5Tl0YJPCI8XYKh3Yapw9L9/m/HGFulSj8XTaxS+IJISJyxlVeFVE2sLjM3RSiJ1UIuF5Z5PPvUJfvULv8JjVx5hPOzz9ns3eOnVb9bVxxERhyG2qtBSESo5LxrqnV+tYVUT0oSbX7/OoQA931l6QAoPUuDniV4fVvPOU86129wxehlaK1TNNKzJRULiAJxDUZuGIAReKFSo0RaElugwIIoiJLVBvBEWoRXSC6SX93cM0sy/90MossIPOYm/vjFFqlr0qhlYEuUJwoA4lETSEqr6wAo8BkGOmpNdHAIH3tTHxym8P/Te9OAiAhlw5byjdBVZdXSymOVD3n77DS5cOM+sMAzHe4QBaC24c/dN5EGPcpjjp4Y0bLHfHxP3rrO81mF5bYnrb7/HxsYGB/0NTvQXSZKAOImprCbUIaowdBot+hs3j1zHP/6jbxE2AlppTCgXKWyKQeBdiRce2W0jtcSFChdpolhzqteiGWtaOkE4yTAruLmzxyviOmYiMBLCjuLUScluNuPOdMalpd4xZ0YgxQe2psw1aEStWVPLAiiYS7AqpSiLuY2cer/1IoSmqsr70sF1yLmTjr/fgnlQDAcjGo2EdiflkUeewLgZr7/1GuunVkAEbG/1icOQ5z7+BEKVeJcQ6jatVsrW1g5lNUQJwfLSCXY2tmg1AzoLPcI4IitOcfu954mi4L7cw4NirCxpWdK9foev/sd/k7XlJfb3h7C4yI/9yi/T+MynKELFthBUqWThiQts/r03SXf6XOv/M8o05MqPPUt7cYFr33wZJhPCoMnuW2/z0V/9AvGF0/OkcfTt11pexVHUphemYGV5CQFMp1Mknm7cZn+7z045YGVthXavQRJfQIclthqhlaQsFYOxQsomYeQwbsTd228wGmYsLV0litvEaevIdVhR4oSgrCyh1lhTUtkKMzXk0xn969v4wELgCEJFkCTErWVaSczUaKaTKeWo4mMf/QS//Q//LmkcMiwzXnzzu7z9wtdYPdniZLfHzY2DI9ehhEVrgcQRCIGScm4H6/E4lKgLEaXmCVEKpPB4Z9CqvsaV0jXrUwGoWnJDAFISK4UVAn8MCziKNE45nLc4oxG+zk9+zuWx1HK32se005RMZbXMgrJMlUVrhzMeqSQ6qO8NcUhcdOCCuvUoP6Sg3w+3Ete67hPiME6QIchLSeWASM/1RuqFGwQVkkjUokpSgHc1o1MJWcshUPdpEbb2OKxcXT0ew3TKTUkvbjKaDDkYThjNBiSRpJmmdHo93ru7h6wEp5bPcuPGLe5t79M5e4KO6nDjzi1erEpWwAAAIABJREFUv/4GOggpUYxmGZU3jLMZUZRQSc1zV58mm0x55+btI9fh8pRksUe7GbGzJ5E6ZXmlQRQ4jLUU0gCGREEiIRae7dmQYeVYTFM6UZO0ozgbN3DRaUwe4ioF2qDjIYEzLLU6PLd+hLElf0zO181viLmc5uF7VVVhncU7j1KKIFR4X1P7BbW0p8PV1Z8QNW1Yyg9o34hjmXDeCQb9CQLHN/7oFRbWUj75mU/SaMTs7x8wGU9wjZisdLTaEZNRyWh/l+0dgbOKSMNwOsVVCkmKEopxf8Z4somRUBYVeVYh1dEVTk9I0iJn9+VXWFtMefzpK9x47Qbvvv0e7/xfX6ax26d79izd8xfIl1qUoSJcWKC6u0NqHY3SsvXiy+wnAe1I45whG/TJG/uEgIwiqCzWHX2d7o4ylpYaNNsCjUbPvVc7a2uUZUkSGvSJLrPCYmyBtRH9QUwQxcRpAtZyMBhyd3PKYucMVZWRtisaaYEzO9zdDGh3V2m3j94RBMLX7QdnUabEG4MwHq0CrHC4qkQKjXLgspJy6jGzIUUao9stFpZP8+zVx/iNX/9VVBwysBWvv/Mm/8v/9o9Q23f5uLzKdDrAy6NV+2rBtdrGT+Lrrsa8apaibrNIOWde17UItSxyzZ4WUtQ7ee/m7Za6mQLznaivdxz+mEo8kFComvWp8eDAO5DzClwiKb0i1pIk0ATm0LugNkH3CESg0FLWDE03f5J4h5AebFW3IH8UnX2UNPVwTEiEDHFC4QBnDYEBLzxW1Lzoqtb3wlQVSnrCwBNogbe2TuaHklPeI0StdGeqEGs51t3das297W1a7Sb90Qi0YpIXxEmD3f4Q6zV37m2hdI/39gYUOqIME/qlZ3OrTz83VJOCJG3gwgRCjXMV03zGYrfHexu3CWRI45gKuLG6yEJjATEBkw/pLFgSVRBLSZyC1I4Qj5SS0joKUzArh5hKEFSOMNY0wpg00FyJVmmETZo6wvmSvekme7M2y40uy+nRPXExv3DfN9TwtUHyfBva7+8z3N8ly6fYyhDoiO7yEkEQEkUJYRASRBFyLllQ983nbTHpMWV1f2B6VHgvED7E2YhsZsmmlm9/63tcvfoIw+GU8ShnMh3TaC3SaKRoLRkO+0RxSCPpEoQaM4C7t/ucWF4mSoLasMI69vYGc+Ng5oJID44Qj97bY++73yWwlq2332Y22KfhLYO33mBn4x7p4hJLFy/S+9jTdD96Fd/rMDKGViBZ6Lax3jCZTWimC9hAYGYzxjsb7LzzNumpU1RZ3d/l5IPnJpvbA86cOY10GT4f431JqBs0kia725tUqUIpSRIrsrxg89YOsnUB3ewQW8v+9tv09/YYj0JWl5vEUUwxnpC0NGGcYmYTxv1bOHO0yqUva0E0LFQmo5rMcFmOtRYrJJQFQrVQjQiva9loZwydtMNzTz/BX/rpz/KzP/lpVpfa3DMz3tve4otf+l1e+Nq3+LHLZxnPDDZQ2ODopCXnhruHHefD9mpdGApUrZqBw+NEPVgWXuDd+5IaUVC7/4xLg/WHrZO6V67ml+cxz1aUVoRe4fBYRS1g7yTe1YPtKI5wrraPU1GbtlbIMMRM63XEUUiFxzmwpZ5LjXiEtwhvcKXCe/lhuyk/bLd7M5efdSjpa42S+XBgVta6H4fyplYAsvbRdJXFOUcoQHuLQ2G1QFnAGIxQGF93zZ2zdWV/RMyMxZuSg/EIJxTj6Qxnc3q9ZfKqojIar2Ku3d5E9pYJkwb7uSYuU06cfZKTF5+kt9ijMoZ3r79KMRtyMBjSawjSVsqbN96h11viYHC0Glp+kHF38x7YgM6FkGY4RbkB1hikVZSTiv2JpRAhIl1iIWmw1b9HEigqJ4milEbapBmlBGlIquqkPy4doWrQiZeYVTPuDLePXMd9fZr7GhMSZz3j8YDRaMD2rRvsbG4ivCXPMoTS6DhBSE0jSUjShO7iCXpLKywsLSOkxJgKKTXD4Yh33n4HKQXnzh2tn117pEoqk+HcjN5Sh7wacPvOLRYXVoiiGKEkg/4UrQPOnjnPU08/ThSF3Lu7S5nlFHmFqQKMF/SWlknSCOM9W7sjojAmn42R6ui7VJgKt72DHPRhWnDQvwvW0Y5DlJcUoyHVqM/+vdvsX3+LE3uf4epjj/LK179NaUcUwhJGIa5QbO/sE4uIJIkZTEbc+O4rfPxzP0816WOOGbBOJlP+H+beLMay5Lzz+0Wc/dz95p5VWZW1ZFVX79ULm91kk2ySQ0qjXbJGywhabGkMYwxhDMOCPQMYfvGT4YFnDAOCPYYEDSyNpIFEShw1SUmk2Nx7766u7tor9/Xm3e/ZI8IPJ7s5fmCmHozGxFMVsu7Nr87yxRff8vtvb+0SOobzp5skyRiEoVavUJiCXj9iZraCH9oMRgO2tg6wp6dwa3NMu3UmowMG3Q6NRovLD5ynGkhuXu8z7HUItY3vCpJkTDTsHWvH9GyTKElwLBchNFnNJxSCmVabVKQM+/sksU2GzcUHHuKBlcucW5jix37sRzl/YZlm4KDQ9LXi9Wvf56tf+zovffVbqIlgbX3A8kWHoL1Au3k8elWaowgbEFYZ7AkhjzpQSr5RidnRRz+zPkBelc+3Jk/GdLtdwvbcUadP+YWWBKHKHPpJMGvbtnCMdfT5Iw72Ua7dkgZvuMfF2Sr/5vf/Ld3ZM4xth3OPPE6ep8RJxGylgisscqUgyNA6R6kCrSRGSXBdjNIYfbxYxwf2/L3+1f9Py5K6FH4RosSVCv0DcL8pO03epxIKYcrWKelgeR6upTEmOQKnC/rdbW6/d5MHLz2IW22jKBng5THkeDtSZXAsh5n5BZwg4+5rb9JsuMS5odmeY7+zj5EeOAEXH3uCseUw7ERsvbmKbQnOnVvi6vx5Dnpb4NZxhEUdwUxDsL27w06vy/5ogu8dX0HL7vbIqhLRCgmkJtYGConOFd39EYd3h8TKozb9GK41z5NnK6RZh3c3dhjPCCxj0fLqzPoBCMXOoE9vMgHt0/anCT2LjeGQKD+hcIVB6fKFMEBR5HQPO1x/710G/T4rLQsdjfBsic4ytJ4wGQ1xbIvCgjz0mHT3WL9/lzNnz3Lq3ApeUGE4HPP1r32d0XBMq9XEtiQXLp7/oVYUKsX1BJW6ixNWWVm5UIqEZBpLOmxtaZI0oT01g20HHHYPmW3XsW1BpRLQ3e+RJCkIg7QlveEYN/CYxAlZBrbj4HouWZYcezXsgx77b76DnedUgpDUzbGTDMdy8GwflRZoNHmSMdraZfPLX6N6/jwiijBGEycxYSWk3W5z9/ZdxsWYWrWCbVscHnYwrg2Bh30CPXB5eZGtzQ2KZMK508/TaE8TxQm98QHVpk0zrFCruex29tja2+Xhp55mtWMTRTlDk3J6egU71sQm4eBgn5GjmEzGGJEyGI5ptRo4jkSfUFA0lo3lV3Adj6ceu8ATjz/I2YV5psMQOdhnbeM6kjqJETRnpzmzMMdUM2RqKmG4e5NJWMepVFjf3+OPf+//5vrNG/Q6KYFXRVRqDCKbWrONfUI6RRQGy5QdH4aycC6RCF2e/jiiANrvI6WNOoJOmaNyO0hi2jUfJWyEKBC6+EHa8P3i/QmUS8uSWEYgj+RbjTEgJRKBIyX64DaDiWLj2vcw3m26aRd1uMGt+xsIDGt37rD80BNcff7TbG2vYvsVHC8AXYqNqKzET2P+E6QYhlaOdZQrFVKUUbfgCA1rkJYgi8cc7G4y7h9Qr/pcWL6ANh6F5aAdF4NEFSNW3/5r9vaHnL98Ba2L8haJknTnW8eH4kuzNlPtOg8stXl3f51L0zNUayFPPvgIru3w5ivvkNdPc+UT/5DNwz7jSQ8tHeSpefIk5tIjTzAcZnzj2hrPXX2QV775NSQZKvexRYu8OCDTCcEJKElZK9CWRno+WWpjXIMvXJJxxmB7gIptlk+d4udXXB6aH/PQ1VPMN36R/3BvlzcO98llgtI5++M+L18b0hvChVOLPHhmAdc3XNu8wfagS2gdH+GowlDoHKNh+/b32Vq9z2FscKSDHvVwihSrKEgHI0yu0OmY5fkFTDZmqupjTfY5OMgpGnO8fnDAKy99jcBzqc4tsXzuHLOzLV595TW+8fW/5ZMvfPKH2jEZZwjhMTN1Fu3l/Okf/g2BV2HlgQtMz9cxODQqU8jCpbsTEUVjiqWMSuix29tj/mwTN7SZnT5DFCe0wpB20GIyKrj57m1s4QMWWXJ8bn71//o9gttrNC1NFMXsj3r4SoKQeG5AtdYgzRO0rbjUnqIoCnZe+T5SZFRqdbI84/DwEIA4TXGkw2G/h6iF5GnEQWARna6W+dRjVugJdKPG0oMPcf3GPtVai/2DfSzPoj8asDzXoCgyNna6bO5l3Nu6wVPPvcDDD13izrvfIVb7PPhQjY39CkpnJGlKs1nFSBjGB6jiAKMFWXG8GxiNC4Kgyj/9jV/nc09Okwz3iMYdksOMwXaHW29+jyceepwH5k/RaGnyvfdYe32dXWnjeDWGCvpxweZul2Bzi5rSiNlZzj/8KMNsxJevf5snPvIUfvOEzUSXHTdGCgoKbBy0BGFJEAZ9lAL8j9N2lpRHvMJyJWNDt9OjfaqKMqUIuzgScNemKOmBJ7y3nm+jszI7YFvvd3OZow5am48//yQvfelP+N1//c9ZmDuDU63x6Rd+iazQpCpj694Nbnz3b3nnS3/A1c/+DLkpUIUpa3uWhz2zRKVawTtBzOX99aE6cZcCWxrEUX5UWmVxDKGREu7dv4MuUva2Vunub3Hl0nmSSY33bt8nxSasTxHUm0TjLudOTRPUWmVKxqgP+KJKGdQJL8fnP/s8SToiimKeuPo4l7KMPM/BCDw/4POf/zxy/jKvrh0yHg3RWYzr+axur/HwykWuPvoQv/t//C4bG2t8a3zIdKPBzvo9inqF+fkZvvXtl6jUfLwT+jy15WPIwTZo3WM4TLFrNebnqyzMzNDrpVScFGo3KDyJlw9oez/GLzz2LO37t/nmzh16SQrKoelPiOOEcTpicwhikhHFhrqosBQe78S1MUSTiPU775IP91AGGoGDKhQ3793iygOzHG5v42OwHYe6IxDpGGUMeZwyMzNNEA1ZP+zQaDfYz1xyrTCjDvNTj7K/vYVUMZ578oCwKgybm7s8/7nHEcJwcNBlMByS6QnVoFnqD3YK2u2psrUrN5jCsLy0jLEU/mKLdnOe/c4ulgStciphSL1eY9RLyLIMx3WPtaEyGVMXhkxl5EmKlRQUBoS0yUVOkqUkeU6j0UD7FuPDHpYtcMOAoBJiJqYUDXAcbPuAVrON0Jqhzskdm8yVTJThhEAcS5QqSmmWMjM/z/Z2j43tfWzPxXZtNva6WNIiyWwcu4LE5qsvfoHN1fP4zojaeQ83rBJWfDzPJRoNsLTCsX1cu4JWh0jpYp0QiSMkH/3oR/jVX/oZbn7t90jHXXqHPXrDlG53QIImjUcMDnaxpYakYDLKSaRG5imEFYJqyPSUYXE8YNeCra0Rd27dY+bcHFNLTXb7O8zVZo8346iCWIrClJuqMdZRBF0WJd8fVTj6AEqrH8wwmDI926y30UaVm4I+KmQetW8CnHSUdyxBYQm0lAipy4FDI7CO1J50lpDkHjOLV3j4qY9x88Z72LYkzVOKPCHLC9otQX84xAkrRJ0d0jQlrFZRWUT33jt0KQf14CePvzd8yE7ct0pnDYq80OQFuI6FoWAyHnPzxhtUApfDvW2iUZf3bowJfAuVD8lzuLu9QaoljmtYfHgOpTRFGmHLUtvx/U6K4oSKwKA/QDoWlh2yN4gxloXnVctCnhXw7MeeZnWouKgrOEIw6mZs7++joiGL000W5lqcX17k2vptuntb5EMH1xJ87LmP06gGrG/dp98/pN083ll4S1M4To6SGdITtKp1Wr7NlO9gUri71eXScpWVBR+yLr1Eoc0Ak0Q81ZqlZlX45tZtukmPxakBmUroJgWD9T6WSQgtyUxdMD9VP9YOaUniUZfXvvW3NEKP3GhcNyQMfKYaDdb3DqEocCpVMm0ojGESRVQrFYoowvLaeJmDmRyyP7rL4tnzbB70OOxuM0q/gu+53L13n3pz5lg7wtDHsmySOGdjbYNKLaTSrDEZJwz6PeYb83TihKnpFlIKbOkQDSM6u7ssnj9NpjMW5s+R54pKxWfQ7RH4AQjDwvwCg8O75dH5hMK3lUY4FFiegwCCQpDIMlpTWpFmGS42VavCOJmQqhzXcRFHQh4Ak0mE6zoEgQ8C6o06eRLhNOoklE5FnLDJe47PzLQLwnBv7TZZblhcmqHWaGLZLo6X0tnvUAxjDBlaTWjXDKt3b2B0xPL8EyDa+L5DnucYA3Eck+US2w2ZpAcEvoNtHS+LlmrNQ489DCKnu73KeDRia7vPOBfsDw9QQpHmMcOxIBcWTmFzOBqSqYJE9nEb07hhnUQVzLXaVMcpvqNJBgXrN3eYv+ITVHyi6PgagdAGczQMiBBoiqPJtrIt+UiKq2ycOIrGtX5fCqIs0leDEInFSKcURz878uQYo8rvOaGy6UobZRVlR4u0QNgU8RhrPCRM97m9f4f7m9vsHhxghEu93sapV/GEIDuapfjpX/9Fzl6+yO3bGQfbm9x+6xUeee4FFs6ew+ghWZKiBse3XL6/Plyh5GyM4qiNzSoVWu5vbLK5uUqaTRgc7jAwiiJPGU0G7He2URjOLi5w+fx56nuH3F7dwBUe127d52B/yMcqi3izFo7roo0uIfH58cdllSvcsEGe2YSteWzPoV6vMzU1xd27d3nymY/ziNfg45lmOBywv36bv/jrb3N7/S4PXzxHPOzxqY89w8vX32BwMOb03DS/9As/z49+5nMURc4nXniOXr/DS9/80rF2GAeU0BRpjnRc6n6VqucznBg214bInstHPxUwXasQ+BdoNVvc2L1Dpgo2BiHff1kwxMI+p7HdCTMtF3ekGAwiVKaYOevTbNpkTnz89Sgy3rv+FvdXV5luNyiiCINgeWEG37VxjeTSmVk6k5zO4YiRyjlVd7Fswenls3iBSzHqMzfT4NbGmI27N1HSo1Zrsb+9jUVB56DL0tnjNSVtRyKQqBwoXMDBD0OiOGdhfp6luQWMUBSmIE1TlFKcXTqDIwR5UiAcF1UIGrWQw50t6tUKQeDS7x+iTQn7LwOyE4afophKtYqyNI49IBeybD0T5fxFkSsqno/My1yqUobQsUEblCrKaMxo4jSh0WxiS484LVDaEAYe2hgcLXGNPnaKNSkicqWwHJ/UWMxMhTRCm9ATDCYxQVghqQRIDimyGMeCSuhTpDmuVyEM51hdHVFvn2Z17YDF+SaF8ekPOlQaHpYMSt1Uc/xJLbA9FuYX6E9GfP/t6yAsJpEhKyTDScYoS7gVDDkTGurGIopgfxSTZGNGWYrT7xE22zhelWpgk0YRg/0+7cVplLHYu7uN2/QIT+jmKooc+f69M+ooIi/7tTm6r0ILzH+szCXeF4opN24pFFmalmkY9AdIiKMvK3ERJxQ2XQFKSIwsmxStpEPl8DaNaB2LnNWkwerqNlHvkGh0iOfbzM3OcfEjiziWRBrB2alpnDQnywpqjRnmZktdVi8MsKIYUahyc/l7rA83EnfKarEBsF3euPY2L33z60wmA5bOLKDysjAkbQlSoC2L3mjEQtZEpSMCKyfq7uK7c2x2B0w3ZmjXQjzL4FgwiiZEUYRjn3BcDmoo6fPok8/ywKWncFyHMAzwfJ8nPjohrExRcas0bQf71Dzywikef/Kj3N+4Tz0M2b17D2ES/tl/9VtMNUNmW01OLy6QxBlKa1rtWWbn5rh9+7Vj7chGMSaJEAKCeoCFT5y4dLsx/V5BEDi8sx3R6R0yVd1mvlXBDhfIsyG2k7E1iLh7Z54HwrMc1hJ8z2bBhulAMsggqAkKW7MVDY+1Y23tHl/6q78iG4+5fOksYmRz2B+w3+uhtGT5dEg8HpFE5ctSGIlwXCxbEoQOyahHFo9pt5tM9TS3t3Zp1OoMOhHGCXjr2nUuP/okU9PTx9qhVI5AYFkub79xg9apKRaWF+l0Dlm8coFRf4DnuqAMeSZIc4VRArRk2B9x6tw5pqdm8X2fJI5YaE6hdE6axtSqFbIsRwgL2z3+sfdyyCxBlir8IGDoj9GqjKA0AkdLtC4odIwtDBSGLE5BSPIiJ1E5qSqwjxSV8jwnSXOEBzVX41AgjMQ5oXA1SWJ29ztkhcW5M1eoBQWuGlJFg+vRH0yYbrY4CHvsb/WQjsOk0Kg84oEHH6bdavLqK2+g6HN25WnGkwLfm0ZkCXkhsKwaxmRHUewPXxXXxXMcBknO9+5vUAkCKn4VS9skcUEvVlzfHaDbhgUHOsOC+7sJhRbkKkfLA6YzxWzDYq2YoMOQSxdCppfOsNMbcfPWu1RVneyE91apvFRykqCPRLd1kWOEQsjyWsr3h3WORu2FLKXaBALbkmzv3ENi4bfnjmTdyhkTc1T8LHf5452nZ+ujTUFQ791CDlcJqnV2kyr9YcRhZ4hUKRVHMeweYLkOv/yPfpqnr16hXvFBSCZJxmCScvPmq8S5ZvnS43iNJkWc4VgWuQRzUr7taH2oTlyKtBwAUQVSjZHJLma0w2h/jyQUFKIcHlFFwWg0wrJs9qN9SDNuXH+P6VaDs6fnuHZzlTTNyEYJf/aXf8LjVx8Bo2k0pqnXmihzfOTpVec4c+UpLjzwFEvLlymKvDxma0211UBIh0JpZJYgdI5UOTWpeXBpkaJImXt0hX5vQH1qGq0KeocdDg72sa0KSmnyoiDLY6ZPSB+IgULSRIQNstRjM5OlepOu4C1YZJbiFa9NdTAgfmUVmexgh2v4LszNNjg9P82vfzzkYGPAjXSK0I9YbEj8Zp3+ZMirhzFREiFPKPS+++4NOgddpqo2ndUNHEdy6dIDvPLWW1hZnzf0HE89+gCWgrwbsb2+wWA4xqbg3XcmDIcTJloQViXzNZvFqw9yZ3sPMxwSWAW/8Iu/TOh7eN7xTssJHHzPIy9ybC/Alobte3fx/ApvvnWLWq2GIKXVDEhHBf39lL++9T0c38KremTCotlqkccOy/OL7B/s0GrO8MwTj7N3OOS733sb1xMocXzr1iQaIkSOtMD1JGHoYuK45FnoDGUZxlmCnqS0m01arTaj/gCEYTKZoKXAcxx0muPonMWVChcfWATZw5nbJdXXWOcCuTw+zZUP4dGVh6g3GuisoLt7izif0Ovb1OZW0EWM0YZPv/ApVi7u8tqrb9J0E6bnznDmTA3PTnj2o4+xuTPkC1/4f7CkxbNPXaVeq7O+tseFi1NMigH2CfWzf/Rf/Dzzc038ap2DoSSJcuIgIskKDvtD7h50CL2Cd/0xjr0G2geT4DoSIUOEqJJZHkO1xm5HM7Mwiz/vUW3abB8miMxi3B9z4cr8sXZI46JNjjYFouAonaIQwkJaDrblAgKdaoSlKXSBfaQDa9BYUnP63BS9wwNsFCpXCKOhyMoTni47TooTJP8aviCwJcJ1+OLvfxUtDLYsaFcqzM1MU29oICUVFq2pOtGgy7NPX2aS5iSFg2VrXCekFng0HM3fff+rZeuuEdTqLV74uV9G5AWp/E+wO2XQ3cBzPUajEe1WjYcvL1H1P8WN926wsLhAlGuktNg/2OfrX3sPEDRm59jaGhGP+qx8/h/g+y6B7xD4DpZl6BzsEIjLXLiwzO5OBxPrEws10/Nn+G9/53+kNbXEr/3Kr/L0009z/vx5XNfFDwLiJEapjCxNkUphiXLoCAFpFCOkxvUcRoM+SiniNKc/HJNEe2xtbYJMqVRdKvYJfZ5WBaMcdKwgysqCrxLYCITl4PoSKQx6qoY4Pcfw5j66X6cS5tgupBoyoXl6ZZbDv9zHLBqKlRATSHJt0+sPUCrF94+PcNL+PktzbaqBy/DwgKuPPk6mNZ2DA+q+RZxr1vf77PeG7B32saVkYzIkygKCIORgGLE7jJibFZyt+czZPk8/dIVwZpbX3niLQhsqtRqefwK0xDJoqdBCYRy3VAyfpEzGObbtILEIKoI4iskmOTpVSNsBJHGcsrG+TbvVZm66TRT12NvvMBimVKsNJlGMkAVpqrG8E4SBgVGaYYoczym5H1JmGKWwBRSWBmQJKBICy7HLGkG9hud5pEUBxuDZDufOzHP1uVN84jMPIMUGhRPzN9EmQs1CWOW4fMozzzyGERlzczPsbewycW1yKyCoNYnSlLn5OXa3N3jr7Tdp1NusrJxjrpJTa1Zoz04zGMYMRl28wOfCSpu1++tsbL7L6YUlJDndXoegoghP0Pr8+KdfYK5awxYGy7VRRtCLDL1JRm+cYPlTJLHHTjRAWBOk8vAdH9vx0TrB9nK8gaYZOHzux3+S9d1VXn/zGkvBAlpL8lQgMokqjneehbIwKkM7R7lsDEZaOKIcs8+UJpAWNg6GFKNTMBa6MAgKgqrDUx99EkHOG99bZWe3j1EaqTRKFIADWmGdcELyhEIIheO4IDULs1NUKz55ktMfDciyjN3DCabISeM+N2++wfziCqd9jzRN6Y0iXnn7LoNIYbsVGu157EE5U2J7PlgW0nH+P101x60Pt09cjXFQNEKQZoKUikcfPMeVldPkeU4/Urx97RqT4SH1qocQguUzp5gMB/RNxptvv8FDDz3MR55+HNt28FyPLEto13yqjuTswlRJEzshp/X4Ux+j3vwTXv7OK7z75jXm5uf4uZ/7OT796U9TqVRYvnSOPEnIophmrU5aGG7duY3RBVmRkqsUbQyFMmRZQZpljMcTxqOIL37hzzm1NMWjj1/hqUceOtYOc0QrQ2j0oKyOCwS64mPXfRy7IE0yCmHjz85SG3qMujnDdILqOjQJsMIa720FPHjxYfbjW2hRwbMsNJo42kGbjOKEZ2F42MV1PGpPe9qgAAAgAElEQVS1Kkka4VZr7Pe6GNvFrbfZ6I64tfMOQeDTbtZp1eqgQizPIRMSr1mh7ecI12MnN6zf2eSiCHj0zAVGuUW6u0vgeSycOp7RkRYJuSk3vtDyMWnB0tQ8FU+SZzFgOH/qLL5vcWD12BkP0ZaLFgW2ZRFFGcPRhHajSrc/IC00hzu7SNGl2+0zO9dkNEoZjo8fwtpMciyRIbTBTTSuI5Gej6TAFDm2JdFGICyJsCzyLCNXBSCoVCqYOEIDgR9w7sJFgopDpRFQqwTE2YjJvfusHjg0Lwew+MNPa5cfvkKejWnW64x7Y5wgROJjpEUQeDQbde7eidhY30GITS5fuozl2DieT38Y4fhNVhZXiKMhj1+1ceyCztYuo5FLrdbAdhwsx0WeUNj8H/6b/55/+l/+Gp/5xJMUVk4RxQwLm0FswKtiiRpFnIJl05heYOXCChvreyyfPc/q2m0OO1s0Ky3mZ+YpSIgluDWPTE/I8jGqSCC22b/dP9YOpQpQCsuxyQtdAq+O0NYYsKwCnecoo4mSHps797mw8hhCaTxb093dIhmcYnquxYXLZ9jcOSTPDLaxMVKSiwKhihMBWBVLYxmFYykuXDhDoRX9/pB+f0jowcb6LuMEJoOUeBJTpIrphVPUGk1QCc3Dfb7y5e9xY33EdDOk1ahjlMGyyzmZ4XhYQrZO6Fd/f324LYa2PhrgEVhIEIoin2BbFq5js3PQ4/SpBS6cO8uzzzzNH/7hH3J6YY6kWWPl/FlOLc5z4cJFtJG8+OKLPPnUk1xeuYAvBUaAcwQ3MiewJMdJgeVUsBwPISWbGxt84xvfIAgCRqMRn/r8J7EwNMIaf/kXX+LV197kZ3/mp+j3DlGmIFVp+QIbiyhJKZShUAWrd+/S7XWotSyyLOG9W7f5hz/6w+0wQoNjYQeCmlWjiCwsW2BXA2TNJ3Q1VqY5E9XpjWMOnBqzDwrGwyFYDkp61N2Qu5Mee29sM3XBIU4StF9jnJfRvGP7J9LQlh94mMF4zHjSx/NcNjodTKF49pln8T2f3mjIu/dW8V2Xs8vnWZiZodVqlGPGloXjeh8ghJOiIEtSPM/h9o2b9A77bPX6NMIKs3PHH5e9wMeyLJQuqHse/+Azn+VHP/VZzGiNjbVbRFHC0ulL5MLwylvXSYarHI5jtDLML8xjV2zarSls28INAqzCoExC5/AQS1isrCxz4711Dg4PjrXjcHqKIk0RSmEXGU1LUNEQuDYoEMiyy0qWUXhR5GAJpCWp1RsoAXGekQnN6u4B65OIUytVPnI1RCeQdBNGmx0a8xEs/nA73nj7Fns7m+hM8dADV1i6cInRZIjvB8zPLpErxbMffZYHr4yYTGJcx8VMuihjoy2PoDZNtbGALSosh00m/Zju1iHrq2sEYcjC0kUWzyxi9PFu4Pnnn6d9aoph3OFUK6TWqLE6yrj08BU+9akfYX93yF9/8cvc3bxFvTnPw088Rpp/h2x8QB73CT2bVrNOQsZf/ts/QtamsRyNV4tI9QgjJ1iJx3Bt91g7SqKSIYsLLMcjywRKGqQAW2qSZETFdTAW1Bsuv/XTv8g3v3GbJJkg8wRHZ7z9/Vfxah6VmTmu33iTpYWLeDgYy6awFCpPS9b3MSt0CkAR2IqdrT2kVGRZ+gECoFqrcNDtsrq2ymQ0ZH5+jjvv3eblV95EiILPf+ppvPsvE29qxnqZQbdDv9fHsmyE4zA+2KPRnsY5qfXzaH243SnaIGUJwpKWhZAOGEOhgELzzjvvcu78eUajMaPRmNOnz7C0uIjrOlhSkmcZd27fZdDvE0/GDLpd9n0f1/HxfY8g9MsWzxMmNr/3ne8x227hOjZ5kYJlkeQ5OwcHzM3NcfvOHaaaTb7z3nf4l//LvyKOEx597AqT8QjXcyjQCEtgUUK7pGWTxDFraxtUKjVa9QbVoMJodDyK1m+3cGUdW3g0ixlyQCeg91z0gWaSa5p5ypbK6RmXImiSTDSunMJ2LCpS49geSZ7RH43Z/2qPxqJHeGlEr15gpE3FreIlx+fELzxwhdZUi8POHpPeIa4f0m7U8R0LpTSntGHp3EXSJC0jPd/Hr9awHbdkf0uJwmA7Lp5fQQjBYNDn3fW71CohZ6sVwjA4kR5Yn2lQTo/mPHn1af7Jb/wmg+0N3nl3jf3OgKuPX0VlNu+8c4ednRGFzkhMgh/6zC/NYHkSbRS37txjaXkex/XIEsWwHzM93WJvb68cwjnhZPKRX/9V0igij2P0eEjv+nX6793EKAiERej5Jc5UlzUc1/VQSlOr1viVX/kVxmnM69feYmdvn73ugN7GAWdfr/PoIw8iCRCFh5gUDHcP4MoPRxF85SvfYG9nl0alDibguU8/wflWg2g8JptkeI4kbE9x5tQycZwyHkfopIW0HXJlKLKMQWcPlVkIz2H59CX0o5Kd7VvEyZCdrXXaU03CyvEnpH/xO/81vckuh6tvcmX+DHKiKSrws7/1T/j4M8+Sp4bPf+ZT/Plf/Dl3776DLkbUGoKrF66Sf2PCXucAocHEBT/5o58kzg1JkZNhsbe5hd8KkbaNdVQI/qFL5BR5hO16R+hkWbYTmpJd32p4XLl0gVdfe5fWXIPZuRZIhZQalUQkk0NW73eQvmT6whk+/SMfo4iAFLa29kt8MoZ663gdgElvj1wYpAopVEzouaWwRZ4xyQr2tg4xaN57+wavnfkuKYJvf+t1vvzi12i0GoSujduex+2P8FyXaq1KlGfYtgPSI09yVF76mL/P+lCdeFCdxpJWKfBgSZRRxFFMXuSA4OLKQzRbLaSQnDolefSxpwgC/0il46iFCEORJ3zyE5/4QNlDyPdzvgVSihPBMcPDPS6dWcR64Xm++8Yr4Pjc3dzg4uhBphcXGE1SkuiAf//vv0itVmeq2ebO/VtcurRSoliPvECWpIzHJYthNJ6wdn+Ds2fP8djDT/DIgw9+wCr+oddj7TSNsE3ncMT6uAdZgsqzkils22it6AGW55IDxshyQEoYsFx2tOaWyTBBjbC9jDx/jsyxkYeayiRhYbFKG4U0x4/d247FzOJpZhYWy1bbI6ZNesRDz7OM5vtcFXlEi7OO+OBHeE+lFbZjk+YZlmVhByHTFx9iLsuZnp1GWi7uCTlxt2poNGsIaQibITduXONbX/5LxtEEQ8DCMqyv3mF9Zx9hOYShTzLpI7SNkZqp6RZ3bt9hOByyfPEUJsnIkhTHsul2O+ztdnAcm1r9eKflnb+CJ0pERB6PCE6d4p6SDG/eQ+aaRlFgux6DJKXINa7tMNuaYXZmlouXL7B0dpmPPPMM19+7yR/9uz9l93Cfd97ZZjx4nOlqlamqROY5B/vHp3UCv8L01By+43H73irKh6XFRe7evsPB7j4/9zOfo3vYYXp6kXqtzWjQZabdLLG8SUK1ajMaH1CpLeCKGjKo8PhjV1mY99jv3KfbixBqQB4dv7nu9/aI4z3ySY/F5ixv793GLC4w9lK+efO7+I6NO2XxI7/8PG983+ada69ROTtN7GuCZpUZk6EsSNt1Gg9VsbWmIhwmseBi82HOyocpsogwOKHCajJmZ2ooJegOIooCXNdHo1hdX+Ohx84xGneYm2/y+BNXSNOIsGIhcoFj+ziVKQ76HWYX5jh78Qwf++THefXbL2MXkq3tHnlmEQY+/gniFP/r//wvOXNxiWo94NvffBlloF7x2NvrMh4l+Jbg4YtLTKKYl/7mJfZGMWs7XVbOzrF0epEXX3yJ5QsXERXFwWEXLwyZsh0QFoWWzMwuICz7CIt48vpwC5uJU05qivfljAS2Xcdyy1H8cyuzKKVK7u/Rf8CVBa7tHkHWy0KfZwUYo1C6QAqouFaZW5YWUkKWH38ceu5jz/O3f/sd1tZWCTwX23O4svIQU40KybhPrTXDF/7sC3Q6hzz22GOkacqtW3fo94doXara+J5P6Psopchixe7mHv1ej4ceeoipVpvJYFTm8I5Zw3tvMgo1JhDI05rW4hStdgPtgrFi5qt1PE/iUGGS+myNRqy9cxPpOjhhFbDRo5hitEVmKoiRRRppyCVpYpAE7FoSqzp1rB1S2Ed9tuXftVLYliCoVDGA65dtWOIInSmEwGhVbq5Hf/alT6FyfD/AIJBSsXL+UolSyI9YECdQDD/742epVgMsWzLa3uHFb/wBKtLEcUat7nP95juMoxHSGjDs7dGNcryqTbfX5+13b7F4OMXB/j7GGHa2BqytrbG7s48fhATVKkpqvLqDdwKKtrAqaHTZelfxECsBj52/hNvvs/7W62y++BUq0RjPBbfqlBS6lsft3ftce/1Vzi2foVELmZ9t88Inn+f8zmVubl3jlbc2+fyzFWZqXR6Yn2UzPP45/e1/9jvUaz5//md/TDoeUJMByTBmZ3sP6dh8763rPHD5Af7q6y8x7I/x/ZD5qRrffOnvuLRyno8/9zRZltDdvI7rlNGr4zhkxYipqQanFxe4fu069269fawd/+rF/42msXmYkEbFQ51zSU+l/M1bf4rt+vitGpYUOGhk2+bMCw9S9xoM3rlPOGVx4fJlFmyHQdNmJy3wColbc3CnXc5Oe/RGA3TunMiSsd2Uyw+cpllr8uJffZVXX36FxfnTPPPM4/z4T/8UaZ7iOTanteT7L7/Eq6+/xtzCGWzhcvfGbZp1n498+hme/8zHEZZNViQ89uRl8sGYSyszvPzKHTqdMXu96Fg77ty+w721m3iBoFlvMtWq8dEnLvOFL7/G+tYN6kHIf/5TP8Fv//Zvsnpvi3/9v/8bpntjfu03fonZ06f5g9/7AzprN1lszfHn371GFMVUajPYXoU0H/PNl15hLozp9beB/+lYW+BDduIKXaJjpcS1nZJAJgVCGqRUQIaQGkvYeLaDJS1s26Cycdnn6ZWbgI7HIDKKPCKORhSeW+Z9bVkO/JzUJG8Er73+JptbW1x5+BJz83P0e32G3QMCW3Lj2nXu371LrVZjbnGBvc4B07UZzl84j+d6jMcjhsMRO9s7vPbaa/T7wyMEpmSq3WBvdxfJNNYJDIaZz9fIAxjHMUWcwYKPU3FI85xMg7IdtOUiZBVXhni5VQ42YCN8D9vzkVWLyqJPuz1LEkG83SFd3YVhjpl4MLEoouMLV4iSMZHnOUKW0b60bZTKAPEBJqG8duW0YaEUrmsf9eKW1/2DL6N09EorNAKV5+XnTwgszp6bwRiFFzhcON2m1xhx87vrCCtjP75PEbYxVeh3Yw76MfWLVbpbPWzfoMg4POwhsbFdh153TJZqPNdn0B/Q6Y/AEri+TVA7/nqUR/SjTc0IhOUzyCO8WpPZZ59D3rhP8u7bNAKbuuuRZhlbkyHNSo1Gs0WeZtxfvccf/dG/46d/6j9j/uxFdv7ugL1+QkGdpTmX0x2LXnb8Jv+lL36BH//xz3Lm9Ay7ayNkkWEVNmu3bvHUs8/w1utv0tk74ODgkIsXL3H16hPsbG7wYz/xExRpxGg8QQrNaDhAq4xqrUYSa7zAJc81tkmZn53BPyGNsdfdpjNQ+DSYMzZ2o4p0LeI0QmQ5VuAwHkfk4wyUxexcg5rjU6gJg6JP3Q65tbuD8GaxPQ+7ZpHbCaNsjLAcYgxITRAezzWv1UJmputMtRo89eQVAhcqlSo/8TOfwwlsjHFQWYrA4nOnPsfVJ5+kUq/i2h7rq9t4ns38hXmEJ7CMjURg2y6esdFZTDzusTh/mswcv7k6GPqjhINeyrnlBUYRdEYpwi6v48qZM1xamqPX2WXY3WRpvkLcq9Boz5Iqm7NL5zncX6c3inGwCRyXeNLFylKQkkq1wpnFJovN/wTZKcIU2FLgSoEjM2yrJBJa1pHkkR2UmpoAhSKNh4zyAVkUI6WFFwYYDFaRUak4WEJjW4JJFJNnGdJ1SLMUecJDGScZzz73HKsbW9y+fZuNtfs0m010nrF+/x4H2wOG/QHNdhsjBM2pNsmgz5uvv8nhYZeDgw7dbpfJaPJBnl9rkI7k+9/7DmQP025UqVSOfyjDxRY4PvYw5qC3QRFDIlK0KpDSYRBndKIMoQqUihjFOcIGIRVCx4ROQbMFbd9hMkoY7I6JOwOUJWDaQy4JTJzD4Hg6nDEFeV6O31vSIssztJZIyy71CgHbdSjyrJSkOhotN6bMS2qjSud/BDMTiFJrUZZitdIuBSb0CRzvatVHCCiKDGE0h90OVA3PPPcYnV6HNFXMnzrNt/vXiPcED67Mszvo4fguS6dm8GSVYW/MeDzh3to6c/MzKKEYpSPQmlqzUor2yuOdp0Fh2RZGQ1EYpJY40kMhmGiYv3iFzbv3qPs2cXfEZDwhHyWcunAFr1Jn87DL2t4e0nN587XXGGqHu+s7tOYCEjXHdB2mmzHZxs6xduys38bm43z+hef4/f/zbXqdfRzXZXl+ChmP+c1//I+5e+8e+XDE3to9XhkPmESKFz71LIKcg/090jSjHjbwvPJeTKIx3cMBYVjB1Zr59hy+PP598WybpMhY6+wSWRIuNFF5gtYFltTEgwE2glazilYg3RwtMiZ5RHWuhj9VYfXeKjPVsygPEq8odSptgVbghQFV38cTx+/yr7x5n73dfR65cpGnn3qUZz72CaSBPE9RqSHwLajVS7SgEFRq1TJwKBSPPHERXAdUUnJncfmgOGIb5CTnkSeu4FcXefX2N461Y32/zyjNsByHLNd4ng2WSzQpI/jz81W+9MUv89UXv8HOzh6ZUSyfW+LO7RskRYF0LXb2OyTFiErokfYjRqMutlugtGJ2po3tBMgTYq/314ebE3cEtiVwpCHwJEKospsERTKJKZSHKXJMnuLbEteSYGJqoQPaIK2jKTjloE2OKnKM0SRZxiSK8AhBWkTJ8U5rEqU8+tgTeJUa/+Kf/3dYGrbX93g/cC4ygXSdD7QkPT/gj/74T8jSH7z8tl3KQyldSsq1WjXCeoWpdoNPfuIT2FKUSuTHrO5Yo4zCxAajXCZRjuva1MIageWx24kYdkfosQbbR4SS9ikb29UIN6biaaZDl7nAYm13gu4NyZMcYXsIYTBZBK4Ns8ffZqUKpCzh9CovkMaUklOmFOHQGApM6dCPxpktyypTYpSCrhJV6j+aH/CbC1XwvmKq0uV3HrfK32ujM4vuXsL2dpdzT56jqE+YnaozGWlynTF92WZrbJHmCfWmx6lTp6nV6hhtkZBiyxRNBlWF68Jcs4FWCt+vMRyOiIvjj8uFSpA4SFGKLhSFxtISI2wKrelkOQOjSZD09w9JJxGJKUi1ZL07YCdN2djdp9ae5j988UsMLAc9U8epLFIIB1sNWZm32XGOf0t/9ic+TcVR7G3doxZY7E+61MMZHl05i1aa3Tt3CBA8evE8SZoyHA547dpNfEtxanGag90D0jhjefl0Kfulcyzp0j3skGWSSOUc6gOa1eODjecf/QjdjQOuffnvuPzog3TtnLmgTb0asH/YKUXPHZvc1lTqdVxPIbGoBhUWfBtXOsxOz1NvTzHIJ+SOjSUgSzKSKMLVPsPugPoJBdb3tju8/t51/vo7L3PmK3PMtKeoBCGOJbFtSdVz8XwPPwyoOS6W0BSuVQorpzGu61ANfRCCHBsty44ixxLoNCUMQvLeHrfWt4+1I1bgBxXCSkg06iPylLg3otsrJ6PHkxH3Ng8YTVJGsSHwAs4tzVFrVqmqgnd3t3njnfuMJgmN1uyRYlnBeNJFoxiPYlZ1j8n4+JbL99eH6sSbVf8DWSVLKuI4YTQaAhqlcg57EaHn4FuCerNG1bOoBBWyKEajSZOY8UQRTWJAUa2FWFLihgHKsnF8jziJmZxwTL13f4079zbZ2d8pBRCOpJsMR8ogAoxSKKWIJhPOzs2SZUXZ/mdJ8lwhhCFwLT7x8WdYXl5m+dwyYa2ClJBnack3P2HiKo1D0jRBD/ugFFkU0fMElh9i2T55rDGRQk8iZDXGdw1erRx0yMgZZRaudqhpjeNAdSHETJfV7WycYiILLFmiOo9bRpDEMQaDI60jxXuD67oIzBHU6QecEAFYdqlVKChPUcaUo9DCKoWUzQd6nJIiL5CWhVLH56JdJ0DlpRyf8ALa89M0piuMol08x2H3oM/W9j7zCwvU5j0KK+Pqk1c4c3aew+4eUCA9hyKvo8wMQgqyXIMIUDkMBylBtY4WxzsLY7JStcZYOI5f1mwKyBKDMRIz00KemmO718GpVtFI8njErY0tqtffY+bUPHt7HXbv3KUWVpCVAHdpgfMrV/5f9t48WNfsKu/77b3f+RvPd+bxzmPPavXckrpbQsgSSCJBQAzGSmwMiauSuOwMtpMihYoEXCShkkr+sIsyhmJwEJIFCIQGEHRL6rn79qSe7njuPefcM33zO++988d7ui2w+5x2lUuIqvv807dPfffWPu+337XXXms9z4NyU6xJWKrtcqu/P6O3iIe8+vzzjAe7NF2PwwszlFlJd2eHcZLy5JPnsAga9RqtdpNaLWQyDMl7A3Z0ThGnCCPob2wSRYp6s0GjVePw/BxlXiIzQxSFHF5Z2v95FCWR7xBiEJ5Ci5xmEBD4AY2owY5O6I9HaOUwSAzNyKVea1IPamBd2mGDzrxHXypCVQcryOMESkWkPPQowTFg5f5ljKEnkZ0OozLjW+d3kG/u4EpFvR5VJtJ5SRRG+Mplrt2mWY9YG1U3+aTfo9NuUnMUMzPTbPfHlKKaqhoMumRpDHmGG02yO9q/d/PAnXfgRA6e5zJTd9HWUGQZR5bmmZ1sk1qP5eUViiwnjlMEkjJJkCZleWWBJ7/xBEmuaTdreFIQ1ltENZ8LVy+hjSAI6/THMVm2vyDYW/iuBvFnnvoWjpKVN57WuI6L3Jur1LpkbrqBMSWB79JLuuyMS/rJmMBxcV0Xja0yZK/y2tzoD6sru3AwQJ6kWCy9eH/R/1/9td9ia7fPKBlghMAgMd+hbPdW0Nva2qKIU1597gVOnZjl8KEVjhw5yvETx2i3WtTDiLIo92rEJSinavoVBmHeNiF5R5TjPkrnCGURxkIKxJrEjzE65/DhgHyhoBcbEi3I4oT1NwvwfKTn0QojaDZJZJ3msqCxJCm0Yq3bZ+3adfKRRNZbB5WikdLB9wRaF3tlhD3LKlOCkG/T9t8SFbLGkuUZ1ljKssA11XVcKok0FWGhmijSaF0dkG/Lge6DKHIpSwiMT+4Zbl85hlSCjjmG0SXNVo1jp6co8zrHT87RmZhka32L1oSlMzOJ50UoeZwiN8TlLl7gkKXVLSOOY65cXkdJjzzbPxOfmZ2gKA1JUhCnSdXgFAYbSRwhaN3zILPvvQuZjim3t9Eb1+Eb36C/uspXvvhlPN+nOTnBwtwMi+89xLNPP0X6yjmeMec52/kA3TCh6Q1ohLP7ruNz/8+vEEhBpCzNQCGsRRcwTjVjY3FxsQbydEiSWoblDsvNGl5WEjmWTtRiotmkVVM89L5bmF86gQglaT5muDtge32DUZrgHMDofeP6NWq55L6734fXUrzS7bPa36TcKVDGIFwXREVFx+aMBxkX1kcU13YIJ1q0vCk8Qlo6YKiHMC7I84xMG1zPxwmhKC1a7y+XsZ5kOCiErVE2pvdMGSwDK0mShFY4QZEU6KLkUgxJuk0SuIROk7rTZqtX0vYUb+6O8aIGozRnmKRYr01S+LRVgUkU2QGTfbefXiLVGUIIcl3QiVoEruLo8jSBGyAlKGPBVjK4vusgTMGll17iK1/4CuevbrOyMMFtx+YQQjLKFb10gkZnkUFcMh7tYGyGEt+DAlhmr0kmlWR+aQ6sZhyPEEpjbYHjCPrDmMK6+LUIrUvKNKc3ztAmRnkepR3j+XvCQnllBiFkibGw2+/hBz6D0f4v6Sc/+UN85U/+jNW1S2xtJSjXQyizN8IIOq/KMePBkG88+hiT7Ql+5r/6NO32BEHgUxQFUkjKsnKRqaYxbKWKaS2uct62iNoPxcY21Nyq/jDIIBOUgEEzDiDJlylSh3jUwxYa8gw8D6fZwFiHnZFh2O8hr+2SpkOEdLF4oDXkJSQpOk4OFPRBVl6FCFU1MSXoIkcXGsf12BOJq24re3VtJQSFqSaJjNa4rrsn62wwugQr0MbgOA7WGJSUe4bJ74ysGDAeZfh+iPUMwo1wRAjWxboKbSW+FARREytGOORMzbRwXa9yChcxpR5RYpDSVLXqXOB7CilLDh+ZpSw0Su7fMHKkgxe5OG5JrodYXSIdFyMlWhuMcMgcgQw9glaLxuIytekZLj/zNFvPPgtxymi3z3Wtuefh9/PcxcvYMuZqL+f/+9LrRMd9jh1doJ/U+fA+l4ITh+aouYp2LaRTC+jtdBn2h4zjlDjN8UtBaQWO41ALPMZxCbqgzC2pVIBgrBIC6bG1uUW7s0Q9qlGr19BZSaPRYJxnjA9IejAxvcGY9dfXmaq3oCHI3T0HLWHIiwTlKLI8J6hFuMqloEB0AtZ6G1jf49jUAlZAMhhSZjHGlbihj7XVrTXPE3y1f22+pgJc5ZGOc4xTYEtNmRd0pjqEXp10GAOycqJ3DG4jopenyD0XnkBA4TiUxjKIB5RGYDxBZjKsC5nVlKaofEP3wVzHJTegHLdixiqDKUoKrTCFJde6utFagVISKQXtepvD9TZzS8vcMhqTl4ayVGSZJsoLmgUs4mBFxHi4vmeYc8Dc/B6+q0F8YX4aYS1KSerNCCE0YcNlPB6R5EOuXb9GUWpKBI7vIR2HpChp1BoVsUKCKTIKY8mzglqtzng8xrGCoizJihIjquCxH04cXWZm+pNcXbvK1//sT9i63mUcj0n3zF+P3XSaZq1Opz3BysIiZ06fZnLar0S39urt2pbYPRcRKSvab5lXh4m1Bqsrkfr98FOP/M2Krq1AaVM1XjyFchWGEhu0ISuxRV65eLsGJ3DwwgCbC7JeTjlOMLrYmy1XSOUgXEGZZzq49fEAACAASURBVIgsxwgOnJIpiqLynTQGUxYIUeliC2urQCwVhbVQVHKuUiqEKarfde9ZO46DMZayyKsms6xIUMZKyqLAaoM6oKxT6B7gg4xxXCjLEqHM3r89ol5vYpFIEVPmLqXNKc0YU0ik2OsDWMN4PMR1fdJxSeA1saXEGvB8i+dIjNl/HXFvSNCsxuYatYA8yynKEmsNjqPQcYlQVQAtrCB2fcLjp5lu1hE1hbe2hdruEQYevbpP49YzrF64wNCWbF2TNMN5xs1D1Jff2aoO4IM/8BB1P6AZhTTDgPFgxLUrq1y9coWtjeu4WwlpriuiDBqrBIUxKCUo8gKLxHMDxgp2u32SNMXJHKwGg8ILIqJ6g93rm/uuY2F2ih2riCeHHJo9hLRjHL/qmQjlMNGJkLLSE3F8jywvqc16lOMhi6YFToNukjHKU2Zbk5Sqg19zCSKHPEvpb44YuYbA259HEOCChsj36addpAHfldg8Js8LpHZxPR/fU+hsTFLoSloAQZrG+GHIKMurEdqgXr0jShAqp5LSKDR+GHCA3Dy9YYrvuxSFpduLcV0Hx1EINEWeYrWm2WyiyxLPq/gRo9QSBAH1WkAQNVFKIKxgFKdoa5BSkmYFSS5J42kEAveAnslbEAdpK9/ADdzADdzA9y7eHSXoBm7gBm7gBr4ncSOI38AN3MAN/DXGjSB+AzdwAzfw1xg3gvgN3MAN3MBfY3xXp1MuX75s33IDPwj/vpnid9uEFUJw5MiRd5wN+dufesQmWQHaonVBu1nNfBd5zige43k+jiPwfcVolICs9A2UUnhepfkyHo2wgCaj1CCEoiwMWmt8VREBPM/nl3/t37zjOu6764RtBR6DUcK17pjF2Q7LM5PMLszSGye89tpl0BlTM23Onr6Z1964xLC/i+t6tJshy3MdZmbm+cM/+yY/9ZM/Tm93k5rvcn23R7c34MKlq+xsbbA9THnhxSvvuI4ojGxBTtBosbh4hmS8y3C4TiOq0e/2cZSoxuwwSKqJlHjPJNgYg+s5+L6q3MJzi5IKhKyErwArHbSxWAzjUf6O6/iDl75skzwGB6Qw+ESEqoGV0I136I92cYOQdmcKz/NxpaAZRJRZjud6FDbHkRohYWA9VD7GdQIcLyAvYxzhkWQ5Qkkemr3nHdfxf/zyP7VA5dxidOWyTiUn4DgOWWlRjvu2iqYxFmmKPYJTidaVYbe1At8PvsN5vZqVN8aSZRnGlPyT//l/f8d1fPqD37+3Dpc4S0EqysKS7LlA5aUGqyhNQlkm+F5AbgQoB1OU+I6iUavjSoUuctoTbaSEUhcIYRln1VSEUop/9aXPveM6fvynfsYeWlrEa+XkdBkOR8TDgsFmQWtScts9y1x6M6c149MfjPACyatP9YhqIWWh6e+MsLqPU4948EOn6O0Yao1JemsjNq9uYbxNXnm+S+R7fPNP/vgd1/ET/9e/sHVX4WKx2lKIgK1BwSirstFCSywexkJhNAKJNQpti78gg2yppCMsgK2+W7C4brVHEYInf+HvveM6zpw+ZofDIUIIJtptBAWOYwl8lzwtCYIG4zhmOEz4wEMf4hOf+BTGjSh1xvPPfpuzp48ThgpdKjJpcEyIJwtKWxmR9LrbbI+67HS7/LPP/NyBerTfZT1x8+/MCr+1wb8zQL8TKeSgOeOD/v5b8F2fJKkC9kSrhTGGYu9LlUKCBdeptM6VVPi+R1lUxqhZliNENe/uuy7jLCXPcxzHpywrDZHqBa0+vx8kgmYjojM1SbPVZzhOeOPyda73xqxd32Brc8xkO6JRDyjTMWeOH+LypRIhHPLSENXreJ5haXYKkm2ULbiytsNWt48rJQLDkZUFZrL9Gay+60CZosc9tlfPY0yCsWMGoz7SCvA8hFAUulIkLLVBlxajDdIR+G7A1OwsSRyT9AcoobBC4EkfbS2aiqgixAGqjrtdSltilaUZBWRFQntmmloQISmYadTI4hi906PmeTSxRKqHoiKQKVWQp7v0h5tMzSzQs5N0paEIDW4gqYmMYTKkMBr24dko6e55rgK2UtR8a39WhiNV8KsCcjU6KYVDWVbuPtVIpwDk2/ox//bnYK3eY7nuvz/yLEM5ewQyK9CFIc/Kao9KQZwle+spwUItrDHu9lCuoMwtjlEUjka5kJcZW91tWq02QiiEBaX0uyJh/Tf/5U8zv9hhMB7xL//NZ8B4ZFbj1AVzyy1efjam24/Z2sxAgcQjHWckwwwrBbOLljJt0pppkgmHVnuWxdYZNp5+jK1rKcunfA4dbeMeQDqquxIXQz0IGI4rso3veRgBgWNwnIAkK0lyKG2A1hZdCqpvbM/Dfk+t0yiJNXZPPaUyVFbKYtEHmqJlWcZ3JqNSKKS0jMYJSnqEfkir2eKBH36QT/3wp7hw8RJadMjTLvn4Io7wcE2EW0ww1gnjfMBgsM7mtTW661tsrl9md9StCISf+bkDVvPdFsAS4i/8F8S+6qRvvThiT7r2oM32bjEeDinzHGMtRVEQeB5ZkeO7Lu12G6stjivwPEUYRGA13WxYfbmlqWY/Pa+ahZaAoKJpa4MjJK5bKTAedOu4cm2HKKgR5An1Wp1aFJBkOevX+1VmWXRpT0xzbbOLG13j1NEjzMzNVdkWDl7Q4NrGNqtXNxnFCde2xlzf2EB5AbvdLUbjjEYU4B4gvRo12uS9BCU02aiHdAzSMSjBnriXwYgqgKd5CULQrDf4vg9/iM3NTfr9Lvfffx/f+OZjtGo1fNej0IY4TiiNIU6Lyo/ygPlsV9Q5tLzAIBmQdbtIGSCcOhaPo/Mn2bn6bea9BrYYoUdjpKyEsqSonHbybIfh1gWy3lXKVRfpTeJOHSI6fjtxGrI22KTRrFML99eLjuPk7eDmKoeSSiemck2HwpZvyx1X0gIK8R1SA3ZP/EsISVmW1YHwHfv3rZ8dJFVclCVm79/MSktZGvI8r6QiyopcNtWZwPEUJs9phiHrW1sURUGW5BjPw3cjCiyNdp3zl6+RlTnNZgdTQhBU0gnV4fPOOHR8kS8+84eMen0GAw9TxrQXGwx6hnPP9KhN1AgbHt2NhOFuQtTQDLsJUgo6M3UcZemPNXfc3OHVpx36mxu8EXS5+NKzjLKEe973ANI5T3+wP83cEwZfuQhjUMolzUqEkQTKJXIUvh9CmaFFgZQCbQWlrAh85jtihzW2MrWvFNuqpEwIEOXbqpz7QQhRSfrmeXUQCChLg0WSZCWf+czPU6vXEWh6Oxt889Evccfdn0KUIxamBd2tFxlpn0jP8QffeJy1rRFWpjQ8n6YbcubYYRZW7scLvgfJPt8Ji6io5nsvh5UCxxiMqEwPvjMzfzuY7/3ICoG0VWCpcpz/MEy2JxiOY4KipNNuYa2lyPPK41KBUhJXOWBspXEsFGWpQRjCMEQbQzoe4zlV2WR6KkJrQZoU1ZU2L99mMu6H288cptFss7HVpaSkWa/hl5bJdg3P92mFLmGjzn33vZdGEOAIy/lLO4xHCStHj7C8NE/dF/zYJ+bYGuaszLcRIuc3Pv9VrLbU6g3qjZDAOYA5KiP8Wod01CMMXbQtUJ6P5wV4foDvuWRJjNntsnx4locfepi/87d+ipmZOZ544glefPkFSp1x+5njDMa7pFmC1hZrAwyVSnCel+TZ/s/j+HtO0Rts4rqKRnueGh7F7jp60GW3GBCWGU7YBEqGvctsX30Kt+YTNuYxJmR36xrjbJv5qUN4w9exyUXqay9TPvk5giM3UfvYz2BigXeA65UQDkpVL7hUEmuqgP1W+UQh0XuBGfaycav2RPH2tF8EaFP8hRuZ1hq5x1w1xh5YHqzXaozjmKIo6A77lGXB9MwUrlvJlSolOX3mJOfPX2F3q0+ajFhZbrO922ei5XDk0DQnjx1hOOiSlSUf+/hP8OILb/DyC+cRBCRJ5Qbve/uTSr723Fd49LEvs7uesr3eo7M0R22U02lYdFvz2hNXsBLcmktzok23m6Jzjec7bK/H9LdyjpxYYOPNFZ772pdIRj2EqDJfU8Ljf/oSjVkHNbG/eUmeaVKtkcpDG4G1Ek9KdFmic+iOhyBcXOHhCLAOlFZRSoGWlX+ttXbPkcnuKZAqhBRvE8WMNQcGlLIsybI92n2WoWQlcVxvTTA7d4inn32OQ4tLdNoNVq+8wvrqS7z44nOcXF4gL9b488fOMRHVufmQR3e9hSmmMNKyqbfpCbhy+SK7f9RnnMf89D/62f0Xw19hEJdGYhQIDE4JtlBkXomjXbCm8p/8zs/bt5WqMSgcnZNLh1Ip3INo5X8J1lrCICAMqkDrOA7KD1CqolULpXBcl6LIKLIMbarPZ3nOeDxGCIHjupSlxo8ctLYUpamYmsaQ5RlKKgJv/+vhc2+sMtXscudtZymLgo3r12k16jSkgxSar71wiQ8+dCftRo2tnSFTnQYX17vMT7fQ6YBr11bZ2e0x3Wny5huXCM+c5I3zq5Ta0IwiAt9hGGc8+PDd+z+PImWyPc1WnjMxGVKv+dTrEW4QgpC4SrK7s0My6nPrLWd54P57eOO15xkOlrE2Z2a6w7GTR3j9zVf41jf/jHrYrNyb9spOw+GQ0WhMfMD3NBwPcGTEVFBndP5FhuMxsrDUPIEoRwy3XqUIQxzlILMRDV+jPI0jEowQ1ANFe2KWetTAUVNcHwxIhz36m9fZ2Rlx20f/MWnRpTxAoyPwA+weG9XajCQeUJZVrdt1HRyvVsnX7+0TY6tgUOoqkyuKYm+z6sryy+4Jq+1p0gghUUru/f/+EEJQFDlaZ0S1gGPHV3BcwFrWr15lshXQuOkkb7wuyfIRwyQnDARnz5xhdqqN1gYpPHwXJCnTnTo6r/RgVBBUQdzff5++/Mp53nixz8pSi3KiRTku2RpmRKcmSUrBcDAiajZodDrEgxHJKGWqESF9D6PACwLiNGB8oUeWDLAY8iJDJ9WhvrOxiSna0N8/A9baYK1ClwZXqep3EBbhV+UMx7UYqylKQ6lV1cFxqNynBAhr0EJghKoMyYVGSbmnzGnRRiCRlXPWPnBdF2steZ5TliWu75PnJZNRg9vueC8rR48y2e4gdMn1rS12d7fxPJ+Nq5c5dKSF60b0h5akGLKzleAGAaNxj1HRw3MVwnj4zSbbm/vv07fwXa+JW7uXgYxidl5+Fb/p0j66hONOM3YEkYhxtOIvD84YAVZU2bgRBiP27MHsv5vRHJjh1OsoxwMp2d7exPCWdKp++3pclCVJlu4FZrFnfGBJkgTHcfA8r9IIwcFasXfK66peZjSu4xxkZEO77rMzTHjt/GXOHl1meXGBXFvqwjBOMz71oz/MpTdfJEsNj7z/XlYW5zlz+gyXLl0hzzKK0tBoNpmYnKax0+exZ56n2x3SbjWZajdp1wO2ekOOHXlnH0eAcbxLq+XSnqhx+MgSoa+oRQGBU30Lnl/DF7C7uc2DDz7E0vIRvv7Vz/Pa+W8T1RrMzC7ghz4f+egPcO7555GikkHI8wJjS4ajtBKTOqg273vEo5iy2EUPMjwRokyCyIaYbJv+xnPkkYfv13FViE1iBvEI6Rny3EWXCdP1KZTj8Nqlq2xe3aK7k9Ld3mHlg/ey048x+QjP2X8dgS8R1gFj6HevMd69wvbWgJ2tfmVlNzOHwWG6M8HU9CRlNIHQValFyb2rORYpqrq32RMBk0JhjEXtaYTE8QHBwlG4rqTfT5maarC8vMLhlRVuvuU0F86/jm9zTJ6ytHCIZDymN+wSpjlRLeCOO27GUYrnnj2H0RarUwbdHoEXcOLkEcbjhN1+8baW0X7IkqtY4PDUPFeLLfpjy+b1ymx63CsotSbPMsaDPsU4gdLi+YLtfp9jx6bxvTqzMyd46pnzYEFri+u5tCdCWi2PtdUBpt9iZWL/w0QY83Yi51hDGNaQ0qUsNI5S1XMWkJWWUW6oXOcMwihcXeKJnFwocjy0sWgBSlmUrSoCORKDqZqb+8AYQ60WoVQlt+u5LrV2g7nZeaJ6i87MNKaoyl/aCOrNFoH0SHZjBv0hM4vLDHYlRm0h5Ygy26IZpEw0IU1SxplBEOI434NGyW8FcAFYZcnOX+LaU08xe3KR9kfeT/PIHXuCUm8VSezbJZRCVkHcwaJsQSEFylSn61sNz3dbM6+0sA3xKKkaR0pijSFJqlponMTUazUshjSNCfwIa+zbtnJ5Xvlg1mpRpY/gRzT8Ghvr62RpgnAVRZkjDzjRD83PcOHaJkIpXjx/iZlWC8/zKMuclUPH+a//u5/lf/iH/wVbO9t84ff+kCOHV/joRx7hprM3kScpwiZoo/GjOvfdex9PPPM0f/zVrxPUSmqB4vD8FEmS8urr5/nYPuuQUjMa9/AcybVrV+m0Wwha+KEgigIslnpYQxcwP7fC7bfdQzIc0Wq1MUha7UmmZ+bp9QcY2WIw7jMep9VBU5QkSU5eaOJsf6nRsc0oy5T+9TVcP8KPpikHa8S9SzC6SJbtIp0QWxZ0h5uMxzGxMaTFkNHYMj3p05yokae7XHz1EsOspD9Mqc2f5eQHfpQyiFndeBPfE7Cy3/6wiNLiOJLu1jVM0qccDSjGQ9zI5+Kr58hLF3FkhZrICA83kU6AdPZKLqLSkHGEwNnzH5VSVtMS1layxsqh2Wzu+zzCwCcvEsoyY2lxgZMnj3Lq1Gma9SbxcESnXRlLP/PsM4xGKfV2k6nJNmfOnqTZrLG9vU2cjmjU22xc22A4zGg2mtx2+03E6YCv/+lLe4bP+5e5Hr7/OO+56Sj1Zo1OeAdff+INRskO3bUxylgWl5toYTl7poVJmiRlyXgsWQgVp08d5kMPfYSNTZevffnxvf6R5a77j/DJ//Re+v0dvvnnr/LKazvccs/xfddhhUDYf9tQLooSazVGg3CcPRE3Q6gkTgCqsBRaY8ocmQ9ougXDLAfjotxG5ZwlqyYvtropaWs5SH80y3L8oPL+NcaSFyVHZxc4ceoMrh9ineq20BvGXF7dYHt3QFRGBMKjP+yiwhYyqpEZg+PENGuGmSmF7ziUacRaT7HWH0L5PShFC7zdIJKhT+eHP4BaiNj6g6/R/+e/yk3/6BB6ehZtcqQt3uqqgagUEJUV+IVB5CPisI4w+i+Ur95qHr2bYJ4kCds7uyhX4fs+UgiMtRhrGY/HuK5LvREhJPiOT1GUuK5bfXZvWqEoSsKah6M8hHAwBvwgJCsSHN+rfCX3weradZSjGI+H1GpNrJCMkoRxHPPxO+9hc3sXY1yOHFnhtdfepNcfcP7Ni0xMjXEcQTwcYVEId8i9d93NiWN9rl5b5+KFywhTEtUanD59hOef399DsSw1w/6IRr2GKTW2EGRJyajlUgsTHKdk8/ouRQmrVzYwpeShhz6BVFXXfzge8vy5l3jjjQs88MGPs755natXr7K9tc04HlNXld54eoD3aSEhEA5XXjnP9PIx3IZBOwYjMwRD+kWLtWuC5fkGmi2ysqxceHCoeRJdjOlu77J5/TJmZEiTnMZcm9MP/yDt42fZTrt4wiM6YApCSonjKZSArc0BDpbBqCAvS0yiKQpNtztmNDXB7tYGnVaH9txxHMfFcRzyPKcwBisk/VGC6zokyYgwDAkCH6XE26Ya+z6PspI5xhpWVlZwpOTCm6/zbH+HN994lTtuPsP0zAyXrmwwisfk1lCvG06dPcbS4gKb6xvYIqe7s8Hm5hYz00soGTEYdtE2IQwdxpnGmP2fx13HmkglECqkUa/R7Xo89XhAfc7lffec5q77TzIclZw9dZSLFzcxMuezv/MYE9Ntvu/hv8G9772fX/3Nr5CO+ljA91wOH1/k9G3HOfeCw6c+fYiLF7ZZOLm/y7wW1Q1dINHWkqc51kpcx0Pq6tZorUHJyg/WFy6ugWH3GvHuFWZn66giwYwyCrfD3JHT9EtDXgoQUNqq78VB5RSRY5JdbJbQmVrA6pJ0eAmyaY7NHiKym3Rmpym7Pcb9C6AHoBVhzaHQOezZv2nRRos1ggiW5j0CCxiPRiNglOVcvf49WE75TpR4uMEkiw9/jM57PswT33qS7X/8D1mKJul8+GGCB+7EdiaoFQ5aGrRVRFLw1G/8JlurV/jgP/kfMUJjhEXa/zDO0tW1zcqiyXHwowDP80FralQHQFUxE2Rphue5aKOpapkwMdFG7h0SRamrE1y4JEXJOMvAlMxOThBF0YHTKV4YgLF4XkC3PyYvck6fOMrGZpc7zpzmG09+BWTJ9u4aSRazfalH2GpzRgScO/csq5euMDU/zU0330Ln4jVeePEV0Jrbbz7JU8+9yNb2Losz0zSP7p/xTc1OEoUBnuvQaTUoiwJdFDRrHY4dPcYoyak1p9npj9nu7/Bbn/st/s6n/y5QoDDU6z633HYGo+B6L2Nh8QiLK8crz0SpyPKCwWDIKNl/U1559nHc7Q0e/eKX+ch/8mMs1HLqYUY2Ncu10uWPnl4nGcS89xaXw/Pg1juce6NHqhSjJEWNB9x0yEEUY8REyNF77uXkh3+CfHqSgdmhHdSYOXGWze39Vfs8z8cWBb1enxdfukyr2cJ1A4bZEA+HpaXjDMrrvPrmKiEZlpKvPPoC8Thle3uXne0ueV4iULw9xGYs7YkWR48e5qMf+z5qdQ8h9w8Waa7J84LpyQl++zd/h1xrbr31NGfOHGd5eY5r1zf4yMd/gPfc937OvfAyp86c4bd/61+ztrnBSy8+z4lDh/nwhx7h0uU3qdXbPPn8yxw7ssu9d92KEpYXnn0U6TYRav+k5//+3TUiKQldy6jwOLHU5id//GHK3KHTOsLFjVmeffENnnllSDOsofw2U0vfzyP3n+TWm04BEEYRH/2xT/OVz/0GUg05dPMRLqz3wHEZZJbGZAud7b8/HMdWSR0CaVV1Nbdqb2awxPdcTKnRSJKyqr2GZsitx5sstm4hcCytmsPE5CSlqvHi5R2eutBlqAJybbCiQO9Ns+yHh+47y9mTh6n7Ab47xFcOQnZZvfhFXvy93+GFYlQZkmjLvAqYP1QDOcZjxHAocDKLV8/B9ylkQCoM6AJBgVZjJoOA2bbHy6sHLOSt5/LuPvYfHyoeYPo9iGqkmUu0sIj38R9j+5t/xsZv/wazr77K3Ic/SHnqJMKAJ1zS0Tbrzz3O4QcfxuiqqWGwiL80ingQpOfhuC6uUzVEXMclzQus1gRBQBSF1XSB1hhtcKSDdF2Ua3GUIghC0jwjK8dYISnShGGcUOQpnqvoDwZ7ErX7Hy5Xru3gKcutt9xEYoYIaVhdvcqHvu9jZP0+k60Oi/NNQFCrRdSiOrMtj6XJOs6tZ5ib7lDkCYEDymbs9gdcuLTK0uw0kxMTzM1MMtPp8Nqly/uu4/ChRcLAw5GSKPAosoxbbrmZu2+9neXFJUqheOxbT7K+vsHyyhK33fGeyjhDCwTVS+SpgFqtRbl5DWsM9UYbsORFjpQSz3Vw8/2fx+JEE6l3ufee0/z2r/8KvrHcdGYO6Xh87bGXOHxokuk2YMasXUkZ6piJWx/i1H330xsNePaLv8+uHTHjZGg7YvL296LCWdJuF+ONGSYhQmiazdYBO8QihGEw7BEnOY6bQaoZxjmTYYt+IhjlEmXcqomWxLxw7gKDQaVnjVFV3VtCLapjjGYcx2xtdxmNEsJawAMP3k2rdUA5pVbD6JS8HJOmBYsrs9x88wlmZmfQheX5Z5/jytVVJqeqPk5eFswvLOD6lnQUUW82uHZtlSBscO6lx7m0usZ9997Ke+98L9evrZOnBa3Ip9XY3+nIqacoU71bRZayORwzM3GM97/3A8zNrPBPf/H32bx0ifDYUWrRFGlsqAWW0HOJwkq7/ezJFWbnZ/mT3/8sEzOaTsdB6hgrMozWSJXSaR9wQ7IF1dS3RSGrZ73XG3OtQelqLFaZAldUlm299WdY24VdR1GUBa1mxMzMBLecPcPh2Tavrna5vt3FDWtIW9lFigPC+NbukD/6+vOYDDphSj0KmWglBE5CqCyB9Mlyg3J8rHUpM4FwM4SwBF7A4PoW20kPHIkjDNKAg8KRFkNFlKuFCuddjt39lQVxsbPN6m98ljQfUN50mvDY/finb6Vbl/SeeJTkW+fQL17mzR+5g2w35U7RwIuHlBvXmTy5RK4stVLgiLdynXcPx5EVI1C5SCXRVqB1ZYxQFAWB71XendpQZBo3qDSBpVJkcYIFvDBkFMc4XkAyjCnSGGtKAjcg8P29ufH9M/GJVgOkZXV1jWFSEAYK1Wpy7513ce6l54jmF9ntjRiPClqNgLmZKUbDhN5wwKHDS2RZwmuv7zA9m/PtV57nzfNvoKRit9/jyNHDtGoRtXp0YO3VkxZpSoQVZKOUs2dP8/7772a2M4kucpAuq5cv4jkOh1ZWuOWmW/Y2O28z4VzPpdVqoZwNfCfA9wLiNCVJUra2d6uRvANejluOP8CXzz1HLgYcmvEZDw2uN0Gj6fP9D99EnmyzurrK67uSybkTnHnfR1m6+w4G4yELKyvM/r2fpru+xu5XPouNM178yldZXlln5b4HiVqLxGWlA54ND76maltWUwW+jxWCvCgotGU4zLFuSWoVofJJCyiKkiAMGA4TsAopXaR0MJTkZQbsac5T6as//sRTWGF44P579l2D4/l4vk86Kjl75izHTi0zOTXB5uY6L5x7hfe/7wNkeY5QipXDhyi1Zmpmms2tq8wtLVBrNWhl0yihmJyeIWrUWVycobuzy+7mAKsrHXx1AI+gv7XFrsqpBXXGWuAVHg8fu4v56eNcXl3jz//o9zBlzMa185y4+XaOHDtJmQxo1wOstsRJwVynxvxMkyiKOHHa56aTHp5WxMPKUg3bpBHuP+ro2AyrFcpaJAqLg5QuWIHUhlAWNEOPWuTRaNYIA4fOiffRCEOEcsl1ieMo8jRmZ3sTVbO89sLTrPYKjp69CeEHHyKyMgAAIABJREFUOHtjovshrLfQQcS4V2IYM0o1YVDiotjcHTLXUfQHluF4QD3yaNbrJNkQL/JQ0kXKEiUFWhtCz0NRIk1Vys2tg9QFoefjvsse33e9sbn3B8qFOVoP30v82c/T/dXPob8/Iz6xwuzEBFM/+EP0T7zG7nMvwL/+U6yAXV/A1g6TY4198xLuLXdQykpY/S3i27ul5buqcvC2ZY62Dn69xrDfJ0sTtDG0lhYwuiSNU4QV6KLED0KUcsjjDMfzcQIfvbVNkmZkaUJRZDhK4bluxfAs9YEkioX5WQI/4tHHn6TZbBKFHnffdQ+qzPn9r/4pj3zfIxw/cpTXX7/EmZOniEKf1y9dY5DkmOs9rlxdw7ourl+HMufI8jLjcYwxBa5y0cZgSrjvzvfuu45hd5vA9+i0W9x913tYWlrAl5bABaMEWrjsbG0yPzuLzktG/SGdycmqN6AN2OoW1NprzKZphkwT2u0OjRY0WxOM45g4PqCcMlxHtqc59+ePoZKS3UHKUSFw/YDe2pi8TOn7M9z20Ef4Gz/4NwknZkhwWColeTzgla1nKKI67tRhaknIkuuw8dLTBGHIofvatCbmyIuMNN3f+UlISWE0290e7E0lbfVT4sygPM1CJJnpRPS2x2zGGq10xYKUDpjKo1Q5FiMseZnjSZfAC6q5ZKUYpyVPPPFcRU7ZB0WRYyu3VprtCZZWDoMsGSUxzXaT02fPIJRDUWpCTyGMZmFxGSlLpibq+K6H8gJ2trv80Cd/EN+TOMLy7W+/yfNPv0ItrONIgS72b6CFjiKTEYWR2EJxqHUrJ5aPU+Q5n//sVxl313BchevX2VpbZ7Q74B/8/U9w7NghhnFBMh6jhEApQavV5ORSxKTXIE9yvFoD6ZSUWHaS0X5EWoQpqbkuMw2FyTK0FRgrMBrK3DAbwuFpn6lOSG2igZCSV1+6TDirEGVOvRZRi2rYWsh1k1MowZ1njzG93iMnZWgc1F4ZZj9MTHbYXktQfhOdW+ZmJ6k5V0lH21y+UtDpSIJmxPpOjyAEQU48ynGNRnkVG9hYUFJSDyMcEVPmKZqcsa3jixLPAeddMmD+yhqbuarRuP99tKZneeL//Zdc/cLnmfkHP8lNp27jjW8+jbqygdjcpbi8w/Jkg7pWSHy6+Q6XvvBHtB7+IGW7jfvvSXYPCuZhEKFLTV5meK5DFLrUQ5+RTii0IE0zAt+nFoU4oqLae36IwOB7Lo5yMEjyvNLLGIzHpElczZnbEimqkrs9oOZ58dIFdCkJfYXnSTrNJg+//2G+8MU/Zv7QUcr0Oo0AuoMeL778KnNTLXq9HqPeFnMzHa6s7dCZnGJj/TqtRo0jh5a4dOkKE+15lBQEgcf0ZJv5mal91/GeW29menqKmalJlhZmK9KRq3AdGA7G+LWIZDzmtpM3E4/GvHjuBT7wyMOUZfW72j3Wm+u6NOoNoKJD51nlwuS4Lo1GA8/fP9M6f/0aweJhTt//Ibaff4K1rfOce+kVgtDnxPFlFk7cxCO3f5D20q2IWoeXX3+K9uwsk41JrJtSa02QF5qxU+PkvR9kbeNNBtc2OD4d0RUxHQxJnjG/vLjvOrS1xGmOE0Y0mi36vSHjzKEUAYM4pRkY6rUml3XK2m6XQZyRlxpBNTPuuIqw5pOhSQcjHAWeFOCAUJY8V4wGCc8+s3/DeRR3ESZBOA79ccLGdo9OJ8TxQmYX5kjilO3ekBVZY2JqjrrrEWea3vYGNTdkNB5x+dJl0izj5rNnGfUG9Hpj3njzKhevXmeqOUHkKdwDJtlm5iPwDI6Xk2xJbj11HN+LePmV13js0W9gbUFYi4jqIaPuBq2lOZZWZvjCl/6MxcPLHF1YQGFQjku/t0XQPsHA5mQyx5CTJEO0sAyzIWf3+15yF8g4sjRNpxQYXJABGoe0MDh5j06zpB4VuE41EfS7v/t5Pv23fpSnn3yC0Sjmh37o40xNzdDrD4lakkfuv5PUCJ547QpPXexiTAYH2LMl4y5lGuMYS7+/y8pSB0+BdSSzcz7a8bEiYu5InZpr0VmXZs3FcwWFtRRGMzO3gud47HY1WdxHG4NyPDa3DJMtEKLEf5fR+a+snOISo7WLOHSM+3/pM9w/6PJzP/Yz/OLP/iI/0m7xwGBAmiW8tHKS9D0nmHvwAV75/JepJTknspjuH3+Z1t134s8vYoOQ3FQZuXgX3pbW5CAsrqsqz8EwQk92CAIfm5UQOEgpqTXqIAUN6VAUMeGemFGaxLgiZKrZYrvbpdcd4TqK6c4ktSgAK7DGEvi1fdcRZzHGWgLfIhjwn//kf8vXv/oHPP7Mt/iFX/oX/G+f+V945KH38Xc//fc5duQI0zNzXLh4gRfOPc0/++VfYW4moogNG6PrPLm2TlSvYUoHx7vMTrfPxu6I4TAlScasr/7IO67jlrOHqddCosAnchQ2h1GWst27Smt6hp/7n/5Xrl/f5dgtDto6xKMxly9fZHl5uRr7orJga4YONx9dwXF80qJktz9gnOcM05zc2ANrfKfmlnjtygXsVMj5y2ucufkI6bjg+vaAW95zlotxSl1FFOuXif1d2q1JksEqa+UGu7u72LJkdvEw/ZlJ3jj3KJPv/ziXHz/PUpJztG2IpSV1YbV7lbsnbnvHdeR5hudF3HvPA/S3RoRRQGx26Q5idFHiOQJtM3ypEQJUrUkYjzE1D2sUnu/heApMwcLhCfKkT+AKWnV3b3B2hu2dEVeure/7PKwuuHTxItNTk0x3Oogs45XnLuB4ksWledbWNnjP3fejnIArl1axUpFlKcuLy4z6O7z55iUWFuZRUvGlLz/GpcvXyeKULM5xRICSFik07gFfzE3zhykLi1CK9twMx5fuYHV1lV/4+f+Tb7/yMrWaw1Q9YmvrIqNRyuaV1/n1fzXLb/3a7zA1M43jKMK6z8//0mfo7l7mjWSR8WsXMHmG1iU21zjKJ/JduOmd15GXAwZJyeOP7vC3P3ICx2RcXbtM1Jzk6MwUQnSqCRYhyZMBSrn83M/+95RFxsL8RymKktXVK2gMFy5dYXv9KiePLbNwbJl05xpm7JGJGCH3P9XqzoCP39Okvztiu+8Rcp5yvIoyMdPT0/zaH1ylFw8QFiZD+MiDTUJjyXPDqCzoxoCJmWxFxIUhKzTWdelnBS9eKlicS7j5aKPSb3oX+CtkbCok1fhgri0iaqJPHmayBqvNkBfyOqPzlzn84U9y30cf5PzVVbrzc7gnTjHIxrz8679N8xuPsvLAfczfcRfe0gp55FdNzgNqSZOdVkVQyHNqkU/gK4pUIV0HG7poWeJaW6n3OS6hVycQGqMrmr8Cyiwh9F1qgUezVo0d+p6D1SVWCoLAPdDbcqIzSVkYrm9vcmTqML7b5PNf/jInT9+E40X8+I//Z0y2OoSRj+M4JOMhWxtXadabfPIHPsHnfv/zPP7cS7hCcHSpjSNLtLZksaS3eZ2279NqKeIDMuBvX7pO4LuEgUcj7FMLW8zNLbFy7BTt6TmeePpFHnz/++mOh6Qm5ZbTp5mfn/8LNx7XdSnLklotIo5TyjzFd8XedT8FXaDN/oa8yaig7reZOnUb34j/OYWN8SemKLslw2HJzbc+gLt8ing4xPVaOKWh27uCX/fx2w26V68ySnq0Zjv/P3tvFqzZdZ7nPWvttcd/PvPQA7rRA4AGQADESJCmxFiWbJUsiZUoieLEUZxKYscVJ6lyJZULOymXlQuVrHJy4VQsxZZLSmgqkihSiQaKogiKIEEQHIBGA+hGD6e7z/yfc/5xj2vIxT4NUrZxDnNDUlX9XvVQ3ef717/3t771re99X/INQ5JEtGfmafZOo22DUbZLsxETiqPbGL4fIIXDOcnyyXmSeIWFkxO+8Y03cFaQVRU3rt/E4vHUk0+QlRbYZ19ljKcVXuBT6ALnDGEQIoyk1fAJA0cchuSlZHamxd7BwdHrMZ3SaDR58Pw55ntdkiRBYIkaCdvbm5x94DzvXrtOb3aR2fkF1re2CTzY2NzEVxI8j5WTpxju7fPSF76ECjs0oxglFX6ikJ7BU5K8OLq9NKlGGOOwBpZaj+OrJl/5yue4cuUdjNFI59Ef7DEcD7GlwFM+f/jZz+GoyXG6Kunvlvz2b3wGGYZceWvAW9cmUBoEmnQyQaCQHvzNj71/HFWVY02IK0M20oiF2GNuaRWnQrS2tUTGYQKWwiKFxej6QjEIfMLQ5+LF8xgHH/7Qc/g8w3i0i5HwwMoMb6zvY5w6tp0yPtgkC27TUE2S1VPMRBHj3YCdrSG5LTmYemivgXCOSTllZ99wsiMoSssoLTCiyWRaIshQYYLwFaUpyCrLOA+YFAaDo9M5ugi8h+9bEr8H5xx5nqON4cc//pNc+6f/hOt72wR5iswsz589yZXrb4L2ePLxJwk6s8ggZqXXYusrL/Pup/9fhnc3WPjxH6N7/gIGdaxB8eLiXC0qVOS1bCoVzSSg0wqJjCPZ3Ee5+tJnIisGPYOMHFVZYYxBm4ogDvCVY3lxliQ+pF57EuEscRTiBwFKHb28ua7wpER4kuefeoo/+vxnGWQZly59kMlwj9MnTrG0sEqcRBir6e9sgTOsr68RBCEffu5DXL21xM3rl9k5yBlPC8BgrGJcWEbZGFwtDnUU7u5OCXxJFCpCb8L8nENGHcob6yT9CWGU8Prrl9nd2SYMHN1OwsLCEr7v1y0S52qavZSEgWR9fZf1jXWUUuRFzmA8otJVPZJ51LPgBXRn5wl8g9SCtbsDZG8O2TrH1Xf2mHlkn+HoGkmjwbgokOOc/vg2/cGIpZOnWTp5GoPB2Ax/rk1q1ml3IMv3GQx9ChFgnKAZHZ3EpRQ1CcQKglBxZ/02UdzluWefBGe4s3YVL4pYmZlhZb7H7Y1tWk0fYyArK7StmKQTfM/hrM/cTJdGrPCERnmSSV7ihz6dXvPIOKqyIEkaBHFIt9tgPJ4yOz9Pd36eu1vb9Pv7NNvQbM+xvrHNJEtZnWtTVpooafDghQuEUcRLX/giRTohDBOk81Gej+/7KOUQ4viRur1qRGEtua64FLbY3tnjj//48wxG+0gTsPjcGYzvMRufId0cMljbY3trm0B4FHlKpTV5kfHrv/p/Ebab9K8NkFCrXUqHNgblC8Qxsgzb22NwU3wb8NJr7/CTL16k6WuMszigsgVwOFtv6xP5PQG3e/RBz6uLR2ENXhDQmelhjON83GX4h+/QnyqEd/Rd1rkLl1hqTimynFs7a9zcWKcbU7fJnMOpmDiaRWuoKsP2XslsYKk0THKw1iPyE6aTnIVmjxzJVHsMcoETjsHYcGcnQ/o/gJX4t+U8v73Taa3JshSD47FnP8hP7P4UL738EquFoze4zJ1/+X+w/OKznP/LP8pbN7aodobMzyac+49+lqWPfgR99Rqd1QWmJ1YRxgfPHdtOCaIA30lcs8HBwYDKlCRC0hlWLI9gfdrmjtHk6YT5Vsyw2qex0EEKiwggH2eErYhGGNGZmWexmsU6Q2lASUWzGZCm2bF05nQ0wjlHO4mRep+XX32dTqPBI+dOotAsLawSRJJK15enANMiZ5TmbNxaY6Yzy5nVJVpRyWtXrlJ6Pp5QKOlQGtJckOUVZVUeGcfs3DIzMy26nQYLsz2SuEkzaZGEMQj47/7uf8MXvvASr736ClciRdMTLM6t1ISoZvNQV8Rw5swZnIOZmQ7W5LW7vXBUZYeiLGoFxCO/mBG7wwOK/IDQzzkwjqgFO3duku7eZeHJJge7N6AwdFqnWD61Qm+uSzdaZlo6RqYgCh03b12jKiYM1rfY3Nil2Vlg8YSPVgnaZuRZAUe0xZ1zeEKhMSSNDv3+Oyhh6XXn61HBYc5sb5Z2u8nu1l22796l0Vuk0fDxR4LpZEpWTFCRRxTO0mgk9FoNwFBpjQqnqCBkYWn2yOWIwgDPDxiNRiTnVwmTiDvbeyy3z/OjP/4T3Ln6DjMzsywtLlM6mBUwEwviZpvxdILy2nzxpT/lc5/9Y1qBIHJTAunhCYGUHp7noXVFeIzGT2UE64MhGwcTfuKJmFde+QZvvP4ORpcETcWjH3+cykmMtkxHGeluSv/6DrasiOOY6bSAynH7G+/SODFDc7ELlcZZi7UaaxzWO95n4M7tPhaLh+Bgq+TkTMAPfeAkMptgpcJ5DuHqS2bnJDgLVtdCdIckQGMsOEugPIwtsRicqZBWcvfuXbbGCndMEnfhWW5Nc5bnYh6f32Gz1aEcjbl1e4+BDpB+jjRjhA1ABWSlJW74NJOEdLfC7Qli32dmdY69vSG6tOTeDKmFRjzkA0/8BU6sLDN/YnzsmsD36WLzO0Xy0zTF2lqfuahKTp08ydlTF3n82Scpnvo65n/7VeT+hHcrTX7qAZwJSco9JuI0jbMP4595kNKzICJsVuE4JlFwOP9tDUgBsWRhP2flQDAWIdNHLzANEiqnmOwd0OkmLG1vcnlyi1npmGkGuMijakn8uEkQRthIIipLxw9Rvo8TFuX775GC3g9h6KO1oxEnfOFPv0yJj1I+vVbM6RMn+cbrV1hZXaHZbCBwbO8PeffWHSqj6TRbhEHE6uoFVk6d4ebG9qFMqSDyfcJAIZCkRYU5RmhpbmaGJAlQ0qPTbtGIm8RhRBL6SOnxsY+8wNMfeIxf/qcB33rta9yZm+Gdq1dpNBpMp1N0VbG4uMjS0hICEE4zPzeDrkq0Liidh4eqj7tH4NqVbzJ34hTW85mfbyLbDZ76iefYefcOa5+9QSNZZfHkQ9y8epXBoKQ9D3LYRgSKPBsRnWwzMkO6rZirqcFeW+cgLdgrDWe7J8hsgS89xqOjp2SKwuBsgbWOwA/ptGcYjgpev3KV4f4+px7o0Gw0cShu3riJ1RWhy+kuLJJmOf39vXqK2Vpmuw2kszUjWNfs307XJ2o0aPeObrdJaomHNEspTYkfJESNBOcpKleLsE3TjKIocF5A0mkwGWzSm51nnKbs7e/xmU//LkWesTLTQHkaYTOcA+0cQRge9v+PrvgKM2UuCYlVTKhivvLKK4xHBZ7yOP+RBzF5iTECWxoa0qe52KPXbVHoiqo0dCqJJzzGW7ucfuwUZeAwlQVravai8bCeOXYgIS9K7KGEQVZaPvnZVzm9MsfDsy1KU6EdCOdqsTwpUZ7HPS13cciyvidSZ42mtBVFlZOOh+we5AwnGcYlh//m/dFszvIbv/U7eGT8Bz/9PNHMGR5/4jTVV97g8stvo3AomyEVVNairUSFXaxUaFJK7ZDacDAaMZ7Wcs1vX9tmMILxpOL0qfOsrKwgxNEnk3v4vrZThBB4ykNqibCQTVO+9eprxKVmNy+ZffZFHt7PWPv8H9P/xO9SnTwLDz/MzAvPE2Ow5YBUejijiG2J8Ypaf/IYVGVJURWEfsCZWwPO35kQhj3euLAMq+f5kZMLDKMWaztDWqFk54t/wgPXJoSRz+qJBdrpGAYgKksaaKyQ5Monp6LMc+K4ge/7x9LujbH4ymOcTbGENGOFlDAaHLA4N8P//ZnfIYkjpBQsLS0jleKnP/4zdFoN3r72Nl/56its7N7kYH+Hhbk5Nra2cc4ggaTZxGnNXK9HFB/TPhA1YUpYQRw1aDWaddIRCiEDPCnodpr8+I//GNfffYf90ZjrazeZmZkh9AMW5uY5f+ECrVaL4cEueTpG+epQwc/VfUqhsPboE8GJ+Yu8ffU6M3NNWjOznP7gkyg/YbY3i54/Tbs5S5lKLlx8lMyzFPk+e9u7DMZ95lfmcGmD/ckes16Logrxwx4f/KGHmZQpt9duMNWCTitGuKPXwxgwRr+nKb6w0MOJAbfWRvh+xVwzpBGE7O0csLc74NTqLF0f2s2A6VyH23c3iYKImURxcnmewcEezlQEgNMloZ8QR03a8dE9T2ELPGt4+omP1G0x4XPu3EV6SytsbO5w5uFH2O8fcPnyW1x69FHy0R6hsJgsRaQFX/3Cl9lcu8vCTIcwCtC6Ojw1TVG+wWofKQOm6dHPqQ18Gl5IS/iMByl7gwFFkbHyyCIPvniRYlJvIsIZPEBYn0bTR2tFmVtMadFKEHZjTl84w9rmGioRBCoEA2mZ4zwfdUzRY3RFWZb4SmFVh7du7PJbv/ca/8lPfYiQjKLIwTmUCuo5fWuJo5hSWxASrSvyvKAqS9bv3iWvSkpdMRyOuLM9Ii8KKuHhjjGfSdOM1ROnefPyN3n5m7dZXlzg6996mTS3bI0NgRPMtX2ML9kdVlRaMKkSpmlO6UJkqDDSY5ylVBaGk5L9g32qqn7/Xnnla7RbbebmekfGcQ/f9yTeSBpIIcnzHCk8Pv7v/NtIKfn0pz/N3Zvw8b/xn7Hyc/8p1fYd7vzBH7Dzuc+iXvp9tor/kt5zz9Kcmcc4h1YWz/rY74L6sz8ZMptp/q2bhrnM8KqK6IeSi9s7PPCHn8Pu79B87gWWn/gAXL/LB2/cwl34APav/btY2WT0pZf5F//8l7m8fZf/QSbMtCXXlkNeCdpcKQteePI8zoljK5wg8JmmBYEnSUKBM4Y8t/zdf/AP+eznP8df+thzTEvDwvwqyyun2N3t8zuf+iRCONZuvMPX37qKxNIMfbT0al1lq5kax7SoaDUCKDPyY9opL7zwPFIIQl8xnQ7Z3e1TFjlzC8u0WuF7Ff6ZM2f4hz//83zpS1/ixq3bYDQLc3M04pCFmR7FZEyeTpGeqFtkxhyaHxjKqqqPskegLAtWTyywN7zFtsuZa3YIRUwQdrmRW77yhS+w9MhF4kbAWzfX6fdvErY0ZqLZ2W2RfeOPuHTiYWbPPcqpyGesM7x2Rrl7m40ty4nlx4m8hNmFo6aRoXbkcSil6M0u0mnEdPVXmT0boP05Tj/5UYZbG0TFlPNPn2V5qc1UdRimmqTt88gjp8GLOX3yLHNzim4jZnLQZ7K7RTpN2RdNLq2eIjdHP6tRGJFOR3zz1VdpLSzT7fVYqiSryyfpBB7NJMKf7/LSH32Kf/Tz/zNzs03+0T/+ea6+8xaf+dT/w5VvfZMTc10CH1qxz2Ra4Qsw9YBs/f0ARXm0po0nBcIDawq+/OoVvvrlr/DRn32Cv/LXHsbZisk0ZH+YUhQezkqajYAbdw5QgaDVkQjr8AOPzo89wqOrMY0miCDEDxxJrAhFTH+/YrB3dBw4je+BrwSB6dNt+gw31/mVf/a/47sCo2tZaZwE6nZRo9nEippZO51OMMbQ7XaZ5gXaCaQXMC4czu+imeB5Fq2Pfl9GwxF37mxR0mT+gefoNhKarTNUpuL1td+jScbpRUEVSqalT2EjNsdddg522dzdY5xZimIDbSp8rwXCYZ2PRJIVjs2tA3b7E27f2Tp6PQ7xfb/YhG9L1N4Tr5JS8thjj9XEEach9Fi+cJHVk6e4+fjj3P3kJ1j7Z79G9tprzP3QX6T75AdRsf9n3DuOgjYGayHdO+B3d3b559WUcRLw1ETzN4OEltSURe1ROL1xHf9blylvJbTHFWGU8Lkrl/m9/g6LD13il155mSeziLWtgstC0bh4kn5/CIfkhqNQVgVgCeIGceiRFSV+KPmRZ17gQ88/T1aGaOv4+jevUL76OnOLi5w6fZaN7XV2xwXNZoeyTPGTgMXuDOtbG5i8Ii3qI1rgtakqQaWPfjneeOMNpIBmUlvBzXS7nD51HuXHjCfpYe9Uv9f7fvbZZzlz5myd5Dpdep0O+/0drDFoW2tp35sbd4eXns45iuroiu/azXcgdBTjTXqzi9y6s4U/NpyZXeCBC+fYygo27q6x0JtFTAtOzJ1k5A6IeiG+E3SbTZJWxPreFpNir57XzxLa3iyzJ1cpTc7uzhbt5tE0cynle5LEEkXYbJE1EvJQsj48oNmZJUEjB7eQOkWbgtL36c7PsHCqw4NWghcRxV0iO2GkCxBQVCWD0YTVRy7W0zzHtP6UHxNGmuFgyENPPU+r3WLt5g2accTZs+eYW5zBFCUeDt+TLM/PsbG5weLKaU6ePsnGresYnRNHEb4vCZQk8COsE5RFhQoUaV67Bx2FxAvQtkSqkL3dPqUtKY2mnLhaigLNcqeFRTOdQGU9Om2LlQY/gEaQ0IwD4oUebT9i0QuwhzpF1lqUdCx3Q84eI4eQTyf4SuGkoBfBXJjTjgWh75PnBhUmSF/hSUWWVeSVxnc+aWlrtm1uyfOCqRljpaSoIIxj9kYZqR6SFjk6r45VH+322oftSvjMpz7NiZluPeDQTPB0iacEyld4YYBSJabwePWbb5HrAiNAWw+kh1IcMjg9fL9BIBXKUwRRjJT3rP2Oxw9EEo8Pj/vVocqdtZZz584hhKBwBs9JROVRhm3mf/SvEF56jG/+jf+K/S/+KVnSxjt7nnYy911V4QAYx6ar+MzgNtdyzeJHf5inHjjBW3/8Wf7k6nWe9D2iWGCEZTwzS/jEReTv/R6Tt99iKj2+ogsWnnuGjzz/IX7tiy9xc2+LgTAU3SbPNhLKMqPRaqCOSeI4RxSG+EpS6QohLNYaTp8+S2kEn/zN36TUJXmeEicxzaTFuTMPYJxBCvCVJPQC5ma6BCrEaoPyFJ6UHIzG7PT36LQ7x54IHI6lpWXazQazM11aSUJVVUzSIc1mm0ajUbvXSImnFDjHA6dP4R/+LGEP7emwJEltbHxPdtg59695Er4fItUiS0fIQYUuJcoLKHTJt956Hf9gl6DbpchLNt7d4K1r79CYj3jkuScZ7PTZ2Njk0hMfoD1zgjfeeINdu83M7AqLS6cp/Jy337zCqSfOsdKeYdC/e2QcUtYWe7UWD5hKEnUXaSxMmWsWdGc7lGbI7XSEdAXK9Vg6cwFP+gRBgrGOSoPFQ08hTFrE7Q5mp8H8yVkeuvQYXtygOm5NpMKTNTvxxMoKS0tLbNxe49VXXmXtxhof/uGP0N/aZv3uJoEfsLq6Wlvntds88+wz6GzKlcvfJE5vroByAAAgAElEQVQCKp3jeR6e8nDa1Z9P1vcylmPisAW+56M8wWA0RgoFUlGUBYHTeA5iz0cqRbPrURlN5PvklUduNbGqtU2EzdB5Ri/0cbj63rEC5QkEBuGOkUNwrjZxAIy1aIZMK0GmI6pKIqsK30jC0CPTmjQt8RqOrf6QorKUVUkYBORpSVpoBuOcRkMzraA/mNaCqfcmW45Ar9umGSv2drcpyoJe4DFOAi6/eZnhYEBvtoe2gnxcYYxgNB4CBqGCemLGqz1SpfCIooAwjIiiiMDz8KR8z5ZN/qAyNv9NkFKSJAmDweC9399LAImReJVjr1Gi/IAoszSWHuTJ//bvMO3f4tTzH8bM9SidrT/0d/G5PQM6jNg4Nc/N6zs8ubTCTHcW2j0+l47oC0mrv4u+dpWi0rC/R1UOqbTFeiHXQ0XTF1TWkPmCaDZhsd1g9vRJZrst/EDWhsrR0fPZcVy7gWdZShLHOFvhHGRFzpWrV3nmmSdxVmMsZFlOlhf0+ztkRUFR1FotUDEajTh3dpFWs8l4PKIoinrE0fMwpqqnA47A888+iy5LyqIg8AIO9gZEUcTS3AJx0kBXGulLPFmr8llrEdJDKfWexjqi1k9xrh7RQwisNVRlhe+HeNKQFkfHMb7TJ0kC8qEhac4Rd2fIJeTNNgmKRrdDZpr4TbiUKO7s3GA42kcpSLMU6xJU0CaIWnz4g88hoi6xH7IchTS7S2TxFERK1Dn6ZHLPAd45hzMGh0c0f4Ze3KVZTKh0SpaPkNIhZUylusStOXRpMA5wDqtLtC7RlUUFCd2FFaLmPEHQwgoPXeQcd4EWxy1KVxs3vPH1r1E9/DAnVk+wuLhCEMW8eeUa195+m9LAzNwCo3TKytISgVQ0m23OP3wJFShGgz7Sluxu7VKWBoSHk9RtAxEc6/XpS4uxJZ7QbG738SOfVihZDEPisMmg1OiqQBuBNjlKODqJo2kVTobo0lCUBUM9QngRBVMEtda6p2A2biKcID9GP9sh6kkWUVGJir2qJM0sUlR0kgYqVAxHGmsmQIWQHtfX+5SlZjJNicKYIIyQnmJj7RZeEOJVFWlmQEpMlaP8ACmO8++reOG5J3j73evsHpSIMGBSGbyogfBSJlVGWvnkhaAsHEhQSmEO7eDqytvH9xTNVoPArz1778l23Csevlv8QCRxqHfAMKz7r98J7dWGp2Qlla7w4oi4yog+9mE68ocpdQnUtkuePe61qCFVSMsLCC9eYD9qs732LuWoz7Vba9gkoOx1EKNtvLemhMpBZbFPPEqZaXrdhEcaXa4NJ7x+5RuceexBPnD+NM0wIEhiiqpAqdq6TfhHj26VRUmSJOiqYLs/IIkDfKXY6m/T6y1w5fKbPPPBpzl75kGqsqC/v8/b71zGOouzkuk0xwnLtZu3KYoSY2rrr1JbnKj1u4NWk0AdHUd/ZxdfKdqtNtJ5LC+tHhq9gqk00XeQher2iDysirxvO74r/18zwhYIVOwzHo/rY7N39IlgvDdCFk182SLs+NxZ36KioJ10abRn6SQtQjHL0swMD3UfYpo+wes33mFxaZbHH36aQsQMpkPOnD/F8vIptga7bE83SVXMUO+hSskkHTDJto+Mww/qqQbhaukEISQyatOIE5TLMa5CeIKg3aXS0Fs8gUMiPTC6li0OAkUQOCqlwJb4QUSzF2CsxNoKsFh3jFRxGOGLFn7pcef6NXa3NphZWKYzu8hCs8vW5jZpYXj08Q9S5CmDg1uMhiN8IuJmj/mVEyAFd9auU0yHsDtE+BYpfapS156SVmOOabeFQUSpK0IZM9zfJZ4JaLYDjJPgJMPsAKPr9zSMCoI4oBE0sMIxzQ3aFsSeoOFCkipGerVpdoiP8ASjfIRzikE2Ono9ghBh6g2y0VQE/hzClSjPgVDs7A3YOygRKOZnE1TgMc5zbFXRarUZDiaMx9tI6WNtnfz6+30mqUH4MU5rnJTHnujLMqXTinjs4bP8ydfe4K2bN7FWEAYRvgwoqpTBKENXEWUF1mmQHqEfIr0ATynCMCJQIVEsUaru39cnJfmereEx3hTv4XuexI8ybIjjGM/zyPP8O2Rl7bc1SIwln0wphKTVUv+KXVNt4fbdYOBpBBUyEpy52AV8KnvAT//VxxHuEqXOCVWAFAHu8PgWS4EXBfgISjTnhEIKSfnwY2BrWyjrNFGgqDAYSpDH2Dw5Qb8/oLDww088zJfffpdGI+TGrVu8uHyW67ff5fqdd2p7LQRJlLC0uIQnoBlL0AonJKtzgqKs2Orv02w08AKfsihoN5vMz3ToHiN5urS4ShRFhEFI5CmUUlgjCAJFGHrfdmQ6vLO49x0qpd5zVbrHTnXOHRoB3zMShkajUbdVjhEE297ZI81L4iRAHRgeXH4YfI9AKlya4zdCxnrCK69fRqSax5/+EB96+i9y99q7XPvm2/RWGqhuCxqL3Lx6jY2d1zEyZ81oWq1lopZPOt6gFx+9qYWBOPwsIKPgvarcGA/nEiIJvahBe2aFNE2Zm53FhXF9gXtYhNTtmNrt/h4R6l5Lqaoq7KHj+lEw1kMELVqdWeLJFnk2Ze/uu2zfeps3v2I4cfY8J2bn2NocEnk+TevzyV//JCdOnmN5+TQzs7N4fszuvmEyrNjtF7UqozP4fq2m50tFIzy68syrEmGh6Z3mzCXHrLH0ZhXrm5uowKACKIoKrRzZBA7GBl1M6uqSirjhE6uQxA9I5BmUX6HZAVtS6BzfD1AuJYmOvlCcTqd131hWpGlK5UG7EWON43a/TxAlhFHd4svzClE5xuMUawyTaU5ZGsIgRgWSXrNBmluEjghjV6sc2vr06I652MRItNF89MPPsb29w/buiGlWYUpDKUFnsJbmaJMipUez2SGKovdMQ2ppZh9PKYLQR6q6TSk0hMqnrFICX/LYo+ePjuMQ33Oyz72xrX8TrLV4Xn1Mr18aU++J30kSEoCz5HlOGIaAOPZl+FchybDGYi1YZ3BO1LOjom4DdOKEwPffm/UW4p5hXK013FQB1lq0rsDVDE6lPIqipKp0/fLVWf3IONK8xIpaITluzhIHt4gCrzbqxZHnJXHs4fmSyjj6wwG7BwMacczq4gymLBDSoxH7SAurC7MURVFXP8YxnKYYq8mOMWOQ0sMdtmwa7S5SeAi+bYN1z6X9O7+3e67tQtQuNfVfiT+TxO8ldq3ryzPvGE2Kfn+PLE9JkojTZ0/j+QHNmQ7FNOVrr7/BR3/kY5yYa9JQHd69/A6lM4z39mm1Grzw4vP86VdfYvf6DR565DFOzS9RdVsYEbLV32Hr7lVsEjKdbNOMjo7DP2TKSSnJsowgCN77vA5HIBRaVrSlz9z8MlJ6pLp4r5r6NheiXq8oimqxsMM1vNcutMf0XoWsKzIpPRrNLlEYM5lM61FB37C1vs6Fiz0aSYzV0J6Z52Cyx95ghBPbDIYjjLXs9fdYXphjf3MTBxR5hlINkiShKhzeMUJtHiWlydgf7DOz6pClZFpMKXcUpandl6QvUL7DUw5faiJfEasmiddkPB4TtCNGZcCgvw34zJ4SxFEXqoxIKiT60Brt/RGGIZ4TCKexUpIbw7Q/IAoCZJiwdmcbHMx0WySBf6gPLjDWHdowCpyQtUjadEJeWISMkCqgrCq0rvONPIY5eu85LoqC5557mi+9/Bpm94CsMOjD77SoNJ6U+L4iCEKSpPFnZTgOi6H6eTFgDaHv02pEJEmbOImYmzna6egevi8em9+J77yBvVflRVGEcw6tNcVhH/U7E4gQ4j2fy3uJ/N6ffzdytNPBtE7KUlCaCpBIJ1Ci3khEJTFRrSRWVbVSoe/79aZySLKQQlBqTZ6Xhz1Fh3O1trT0YvwgxD+Gdl+UVX1icgJfQiOKaDUT4qQ2Ya31SGpiUmE0ZWmwzlEYXWsld9uMJ1Pmugk7wwxdlgRhiFIeZpqxezBFHrrVHx2Hpiyq2t/T1j9DKY+ychhTEUVRTZb4MxcOgqqqDh9EWZtFi7r1oL6DLnxvI7bOoY8ZMSyK/FB61TLNM8r9PjIJaTQSFlaWAA9TOE6fOYfyQ67duMpcK2ZldZFJnnLljavMLM2ST0ccSM31u2uUVtPsxCRKkrQfwDRWuXH9rSPjuCchrJTCWfceGc0YizG6nsF3Hk74WKiLgMPnrk7QcM/Vvj6R3Hs2Ofy1xPPEsZe9SnlIqZBSgQtpNJv4YYsszRhPJhTTXfb3dmi2VxlPSrT1kEGDMGmSlwWTyYQ0SzFGMxzsEYQ+4KgqQVkWTCcWIXzKY2zzrInRlWBjd4fSTUjTlNFEEUYhnid5+/KIqBEc9nw1Sgl6zYDQS5lpNbDC4ryMvKxYf/111q7t8Rd+YpVz53qoULLQFSgJgTz6hJTnOdI6fM9QuLrQyMqS3YMDomaIUDFxqOh2O0hj8fyA0irSPMVTPtqUOCFJswIjFNKTeH5Alpek05TK1gYnxyVxh3vv3mRxcYGHHzrH3MIBG5t99g5GTDNHoDziOCEMw7r/7fvvbeL3NnjPUwhRe/cuLc4TIFiam2NmrkMcB5T5d2fPJr5bDe77uI/7uI/7+MHD/z9zyvu4j/u4j/v4gcL9JH4f93Ef9/HnGPeT+H3cx33cx59j3E/i93Ef93Eff47xPZ1O+Zlf+KqDmgzhCYkDnHX4OKRzJJ4iCQS9TkLgQ5aOaMQNrNZI4SGFohFApxkyKiGJAxqhJA4lngy5crvPpHRYqfh7P3v+fQcPo0g5IUHFIQiw1qDiCKUCqmmKTksIPFpn2xT7KdVehbGGqBPhdyOqokJWknKUY/IKgaP2cr/3I0UtQ9sQTPvF+8bx9//WWafCLpPRGCFi+v0xkfJpNRRGD2rqdqGonKEM28x2YjqLCcsXTtFbXCZ0HQ52b1CO9jCDfQb9ddotDyG6mEphMsvqqS5W7vPTf+e1943j5Zf+8HCGE/K8wBldT9xISWUqNjd3uHV3nTTLcc6SRCEffOIJut0OzsH2zg6TyYQgCDn34DmCIKidu6uKLMs4ODioHWCk4N//2Z973zj+67/1t92t/W2SuEn2+m3yjXW658+y8NRT3NxY5+HFOa784RewacapZ55l6cnHGB7chp0Jay99Aa8R88CP/FVevfwmlc546MJ5Nr72GtnGDkopPAHW1ZMmv3/3yvvG8Yv/+cdcHAZEgaKdJMR+QJVPka6kEdaUaYv8M2OH6IqyKOjv9fGkR1WVKJOyu7lIGUX0zq2QCfCFZlSFBHPLpH7E3/vv/6f3jePUhUVni5IojEhmuxSVZm9vjzAIscZQ5QWtTovKaHqdLnmaofMcZx2BUhgcfhQxTnOMdoTKRwpJmmW1Up+DKAgp8pztrb33jePc86ddcz5CaUGv1UR0QXZ9ttd2MJmhSFPwJN1Wl/H+CM+TaOWoUocKAzrdmEYwy8d/6m9z7uJHgdolCGo/06qyaG3Awc+8+MD7xvG//tIvucce+wAL88uHmiPgKXk44lqPw965c4utzVvs7+zQTBIee+5p9qcTFlceoOUlWK3ZzMfsDQ84t3wGU0G/v0OlC5aXF9gb9vnkZ36D/+V//IX3jeOVK7fdJ/7lr7O9vYFUhiiKMNqycWufL33xy/zCL/4D1jbfJi1KenMrPPPsR2i3Gnz1K3/C9Xeucev6VUbDfYJQIkSAJ32WlpYYjSZkA8eVK29y+oGTrJ5b4ZO//CvHDlB/T5N4mDRxrh7H8wHlSgJp8QNQnoeyipkkJvIFjSTAn22T5QVVUVIWFXmWEQqL9EBWIVlRIJIAWwl6ScUD8x0mpUGoo+nuAsCTWGcRvsNag+iEqEYDPdSIsMLzaz3uaqixlUEIR3GQUwyLwxlwgbP1hnTP7f07/3/rHIyPnvxZ38sI2xFzS3OIqiAsmkyGjqkucGVOu+GjhU/SnWdu5gSYMaLa5WBDk+6MaMUnkNpjsKuR0qM/KmgmAd1Wg639cc1Ym0wR0XHaGPW4peNQb9k5pumU8WSC8HzevnGH3f4B1ho6SURpYX84wThJo9FgfbvPdDql02qT5Tmj8biWBshztNYopfD94Fi7On9a8PDcCk7Au8WIRDn8fMKs0DRWF7H7u4Q6Qziw+weYzR06KmR3sINwPo4IIROcCyizMcao2hyAQ6qBqJ1sjqPCRc02vqpH0JwfUXoB2neEXgMbHIq0HZKiavKGh80HyCxlIUoYjYaM9/doeCG5dESdhPmVFaYGAl9xcOV19suMLDl6DjgvSpIgoKgq0r09SmPAkzgBnlIsn14iCCKGoyG7O3sESpGNJ0TKw1YSlEI76DQbDCdTQuVRaQ3WYIyl3ekwHU84zvqp1WrSagd4uSLOG/iVz2QvI8hCJgdj8lFBMhOBq2h1GlR5hVcGbN5ap9FukqgZXvxLP8mj51+ktBacobbCrcf5lKs5GO4YpuR0OiVLa0Ny3w8Ag6PmaEymE3Z2dhgND2j4EVWYsHHrLisnTlIIx251BxM06+8sEMy32tiqYGu7j7GGxYUFnJUYI/H9ox2Xbt68xsb6GsPRPn5gyYKAIq9YW6s1/d988zIbu+9gBYzGGSdPPMimy7j8xtfIxlPGozHOCdK0oNNOAMFwOOTgYMj+VoqUAmM00+nRDNZ7+J4m8dVeQl6UZEaylBge7LSYjRyKog4kTthLNVMtmGYFewc5Oi2JZM7J2YhHL80x0/EIAsdX3y3JRQctfXbHA7YPAKHQFoJjtCDEnMQJhwsttTdEiJY+NtNoAhAl5bSkGu3VJJagppBbA37og4MqzZGinp12kkO+/72C9vBhPG4Pnfhk0xHbw4zlZ07z9NNdZtotdOUY7aXcenOHubkFyknJ2vUNPM+STXYxwV2CqEVvaZ+FxdNsbK7jF3tI47O502b3IEUKTaOs2Nkr6F04WrXPorHaUpYVg4MD7CG5B2e5e3ON9a1dBI4kCojjEGktZ0+doNPp4gcht+9usrZ2l/2D2rQiinxOLC+yMDdHt9ul1Wq9Ry0+Crfe/CNUJfCFxvcU4YyHLTd4608+ge9JbBUQRyA7Ffn0dW68/AamFAjl0epWFLGHCA4I1F2KYkisLuGpEmSGkAohFe5wfvsohMsP4QlBFCo8X4GnaAY+yjmoSkJfvse8w9X8gLCzCHmOrzXebEG8lGHJKOiTrHS53r/B21fv4ndXeHDxAU7NdhDi6PnsRx9/DGEsb731FirwWZxZZngwwGiDJwVbd9dpxg3yvGCp1+PcmQdoJApXlJRZRqvbpXLwrStvEQtLOjzAOldvUFKRFSXOk4dci/fHRz7wYUbZHg3RphpWpOMUZaJamM5XzM+2uPTQBb70jW8wLDKq3FJmBlGFiDyg2s3I7t7hE//k7/Nzf/0/xgtavPHGG3z+j36fF194hoceeQKjfD7z27/Nx1/8xfeN48WP/BitXpexsQRFhjWaveE+m/0dBuMB/8Vf/w+59tY7vPLlVxlPLFXU4Y1376KUz37/Mkk7QtqCW1/9GieaXYbOErR7FGXFRBvuSMPYGPb2B0eux//5a7/C3t42vi8p0losbTSaMJ1M8DzJ+t1N0gyKKidQU17+4udoNVqkgwlFWlIVlv2DA4LQo8h38f2AZrPW8E+ShOHBQf2MumM0XA7xPU3ijzzQZXd/zPrehIZwKCtxFkQQYCrN5GBAZRVlJTHaESuPpBWw0E1Y7iqcs4xSQWx95ucSNg4MqdFIP0AZSRh4lA68Y4b1aYJwAqccohQQRahWG1FUeMZhCo2RgPDAOJxwCCmQymKkrvn9gcAZh6c8jARRWJyrvcy/W0xzh9aGtvbY+cZlqrsJu902nfl5gmSG+ZOncEXBzuYW2XRK0EoobMTCwnlWTl5kbuUkSdJjZv4s61d+E1NMCDvQbS9SZpa82keGLSbHCE/t93dIGi2MrQ1ls2ntJuP5ChWp2g/xUC9lkmU04+SQoelwVhMGijj0kZ5iOJkymVYMx1OWlpbwfYWxmjLLKY/Rrf7Yv7ePV0l8YZhqDw+vlrWVGl9aitKnKh2eH6O1ReeGcgLCC/GsxYQevbPvgLdDOdIsLr9Lw47IThZYmyOlh7bmWMelQHkEvqo3rdDHUx5KSNLhiNH+Pk4IllZWaTfbyEP9GOcsMjEYY4isrZ8DlzMwW9zdvkYz9Hj20gXe3dpmbH1EKnnk7Nkj49jY3ODh8w/xwJkzbB/skU6m2LLCGYvwFJ6DKs04tbzEo5ce4daNa1AoAl8RxCGD4QFSeizP9vjI88/x+c9/njPnLvL6m2/XhQiAFFTHFD0vPPcUVV4R+03+xS//Kru7+3SbXXq9Lo1OizPLiiBqkQ8diyurzHZ7DA7GOGvpNVo8/OAFZuKInZ0NfE+RxCHT0YBXXnmZZ594hHE6IWr1uHntxpFxvPm5zxH22jRmOiwuLHLj+k32R2OSXguHwdiKt9++wqc+9SlG4wl+HLJy8gQnTpxgbe02riVIOh5TJuRvbrPhSg4iD+kEWluGZUFuQXOcDEFGq9tmOp1AUYKxOK2RUvPIxQe5eWON7mwTbQxVkeHLFuNRTjF1eC6g2+mS5RnGafKiRAhJVVX4KkDKiqoypNmIZn40+ekevqdJfK4ZEkiJ05ol3xH5Aus7jPCwCALloUtNIiShp0liy1IrQGIQWMaFRVaKSQlR4mjFlslEIz2F7yyhGxInXQTHGIz6Emcdzgc/8ZmbX2I8gnI6pZd6POXarAU+6xrS0MfDUeUjvNjHqQKbpwglQTts6Q5dcOr+fm08K76rVG6cQSqBdSXzUYAZlYwG++zfPuAgdyTNJdphQkxMJ5JUwhF1GjzxwReZP3GSKAmwBBTjmMH6DHoyRcoSk0+gqjh7pkdWtrm+vnFkHJXW7O3t0mjU/dUgCvHDAK014zTFkxIlAVebVjg3Jc8LwjBHa49mEnP61Emcc+y/eQUHbG1to3VFp9Wg2+kQBrXwz1G4+HSK1YdGxU7iSRACHBYhHJ6s0AasyTDGoZTEWocTGc54WFviBa9y5nyGLi1SvIF+fIB1JQKBMRJjPMQx0qtJFBCFAZHvk8QhvicZ7Oyzfn0Noys2D3YxztGdeQzlB4jDE5mCQzmGw6ToYuYWDN9841Uub9zlkfPnOTMb83b/Nk6scn3r6Jc0K3K+9tqrnD93jqIoQXjEfgieI0liGnMLCK15/NIlNtZv0wg89jbvcuGhiwzTAs+rxUxPrqxwYnGJp554kjBJeP2yBeq1E/J45mhepjxy8VFuXl1jdn6Gnf4+/d0DTFbRmW1yevksd7enOK1oNWNUYOj12jzx6AXagYcqFCsry5y8dI7X373KdHeL22trdBaX0F7EzZ1dmsMJwTGqn3/wO79Fa3GO1XNnePTRR7nx9lXy0jCnFwnaMcZp8GBlZZn5ylDomtXdnZlhrb/HbrpLJ4T5pS6bNwccKJ9pGKGERGiD7h8gDByjQgCH2lRFWREYR1HlCAQnTp3gsYuP8/JXv451jjAKwBp8TxCKkM28QmLI0jFCWJx1ZFlGEscURUEYhEgpDpnthkbyA5jEd/sFRa5poFjpeQgEudFU1pFVmtm4iaWW6PSl4+RCxFxLMZqU7I0LRnmOEDHWwAPLIZ04ZJBWGOsxndzh7u1X+cALf5kwXjgyDmHAawtc6RG1QppBk9zukSaOxRJOPvth9ovs/2PuzWItza77vt/e33zmc+48V91bQ1dVzyx2s5tNmqJIic1ZdixAiBIkQeAYzoAgcGIoCQwEct4CBPZDjNhIYjmiLFqmJYOUOIoSJaon9ljVXXNX3brzvefcM5/zjXvvPHy3O3rhuXoQGl7PF4VV39nf+tb+r//6/5HtfWQ4RoUhsmKjRjGO5SNqBj3SmDDOi3hqAHPC9ckxS6P0qfBBsajIVILrQBTZ2K4NpLjCxs6gkAjMOEEKH1vYxFlEUKnhOFUCt0I8OublV36KbdUpu3MctneYbpTJhgO6nR3K5QWSNGb2FD3xQrFIu92h3+0SlIrYtkXg+wyHQ7QCYYlc6F6AI220zsiylCSNIDHMz+amwa+/8cYJxGTQSnFw2EIiOXvmDJVyGc/3J+bRHRRyeMuS2E5CjnoYhHBA24BNEqcgFa5rk+aqqjmWLy1SZTBhgsHgOIJUx6SegzzRcdaxwsbCck6RCC4WcCwL18m1ntMootcesrRwhqAUsPvSD7h//y62I7ny2OMfyptiDPpE8yc30ZDUG3WWVtcZRhlebYo4C1loOOwe3OHe7Tf5b/7+P/y5ecTjBJWk7O8dYAkQKuOpp55gOBjmCpFC4lqSt996k9XVRXy/QLlsE2cJ0hEc7h+iM02v02U0GDK3vMDmwy0uXzzHW9dvYjlBLgHgTD6n+/s9rj7aYG/7NQ5b+yQqJE0VgyhE9SNuvj/NUaeN5Uk2Lq4yNVtk89Yeo2GXhaUVsjTE9yRKGtJM8/77N+l22xjLoFyBUglbOwcsLc9PzOMwjmi1mrjzs7zx1jW6+0cMBwOOu4ece/wStmtRn8rtCF3P0D0c8MzTT7GxvkESCqZ3JYXOHrXRkEG5SlSeximUMMKQhiOyzSa20thm8o0xSxKSJCGJYmzLBSRKZViWy937D1FI0kQhERgN0ggsFQIRiUkJx33Gwx5eIUAIwWg0wvd92p0O426CZeezqdM0jz6Ij7SIPzjq4KOZK0pKvk0cKoTSaKMZZzFBJIm1wgiLwJNMNzxUMmaUKvb7Cf1RD0skWJbPGe1RdCTTBRc5yNg72mJ8fEA2TrHEKW7VyYkUZmyTHcP797fAjjB1m45n81vXX0W5DiYcwTDEmprFKhRJxvvIxCCDIpFuITUnojoGjAAJ0rFQsQYj0KdohdRKPuNxgmtnhGOJaxJKdY0RmilniqpTZTDMOOqOsCtFZpYusLZ4hjt7He7s7lItKeaW5lleOsedm33wF1H4uHJAsVjlqJXRH7YJJtcspLSpVKp0WvrOATAAACAASURBVC10lqEFjEZjskxTKhQoBiOM0thWzgCoVasYo0jimGKhgOf73H3/Psft9odsDa3BCEl3MKRcLiItSZpMhnVeemvtQ70Sv/iXxc8kWlmoTKC1wXFz16Q0zbBdidIKlYExkiw1KKUpliFJIFW5/kiWZaTxENsyeJ7LV3/p5+fherlptGM7OK5Ls3nE4fExpTNVajPTrK6usrO1zTtvv0OxVOXchYvYtoP84KPtOGxubrK/f8AjVy7z3LOf4NKly9iuS3c05s57b/HI6izhcPJHzc5sHMumc9TliSeuMFUu8Stf/hK3b9/mZz97Ayyb6zduUq2UsQKfZveY4/YBSiWUSgHzCwvs7R1w3GuzdHaNMAs5f2GDe7cfsLa4wHazg205OMXJebx/b5v7Gw/Y2X7I3bu3CIoBqU5Jjc9wKGi2x/i2x9r0IoP9LlJlnD17hrnZBiXPIytYdMbHjCOPcUdz5uw6KUuY8vt0432mrRU6vUPu3789MY9B0UU6Nu00pdceoKKUYq3BzOwMrjDoJMSoDJ2m7G3vIQxMVWscHzcp+Cl1O8OONOFhP9ctMTFCFpBGE8cRnmVjGY17CtyWRPnQPo0TQgxRHCEtm1SMuPPgLmfPX2Jxfoqib5FkEZ1mBxGGONLQ7XWJwjEmUwwHA6RlE4Yhtm0TR2OyRCCtnFzQ7fYm5vHhOfkr/dVfU0xVPTYaLg0nQ1gC4RtcS9Pc3+W1l/6EqfoMlcYs4XiAS8YrL/cwVkyYCMqVqbzTUoK5qSUO2xEHd/fQlkBnKXff/SGzjVl8xzDTOMVuquYQNbO8k3QCFm0HP3M5toqEBYU6OMR3LRydMUaS9rt4HYXtCpQMkd0iLg0y0UVWBSYDExmIDCpVOZiSyx5ODL9iI1xFrewwN7NMqVKn2xswTgs82NP0shqzK9N88jML2I7EGEW12mD7rWvsbj3k/MYGZ+tVjg53qU6dZdQZcf/W22Qq/5qfPV+gNFUgTaLJzyPwERhqjRpplHDn1vsc9oacW1vm4vl11s+cYRxGZCpFCPB9DyEMtVqV3f0Wr7/5YyxjTrBzgSUlaaZIs4xut8dPX/4ZczPT1GuT2Rg3jpY/FJpyHBtEri4pLYFlGVQmsE+00S0rxxE1BiFz2OAD9UClNAzSHE7I3A/pbFpmpCo+1fZqPB5TCgo4QQGtFNs7O1x5+gme/tgz9HptHoseY6rcYDAc8+7b7/LnP3mZz/3iL9Dv94miiEqlwsOtLTYfbPLay69y+dErzM7P4dgOVdfmE8+8wK3bt/mz117lf5iQR0rMzFSDK498nHLBY/v+fb71rW+RJAkzMw3u3bvHlSuP5AJX/S7aaBqNWVaW5/PZRhixuurjOj7j8Rhb2zT3m8zNzfHiF7/M//5//jMGg5hhbzDxebz4pefJOhEPHt7iP/kHv4Q3G/Dun23y3vffp+wEyHiIGiScrVbRvQw9HLF5o8WeayNkxmAUY1s+WWJYWFwljlL2DvZwqg7vE+F+3MWed/jSU5+amMeZeZugPo1TsplavUx9aobp6QaPbaxx7ZWf8hv/1W8gbY/5pWUuPfoYSRTTPDritTdfpzVoYxlDIyjyzKc/z8dm6nz/j35I892fgWcToiiemcUpBMSnmFMc7e4DuR/AILPwg4AkjkmjA9bnKjDusHd/wJUrl1laWiDwXaw05dmrTxGFY2492KUfxozikN/+1/+CoOBRKpUwekg5KDMa7SMlp3rSfhAfaRHfmA5wVES73QfbIdNgkNSmzvDxZ3+R3/vGP2b9zCoLi4vMLizS66bcvXeXNIU06uUqcEqhx3ucnV0mHcfsNMfsH91nc/MBnmUTjXboHnXh/HM/N4+kp5DFHCJQTcPYjbG9GrPeFON0RMd3yZIIy+SKeoEbsL7xKM1uE1fBnF9GaMF+Z4fdqAVlQWHeJTnIyEKNzjRkP19y94N488Y2K8slZArp/gOm05BCMEWUlZiem6LdiqjVq6TxCJNqSpUiBc/gOx5haDEOM4bjMUYryuUGblBB2ZL9QYhf8qgNDN3uHr5/CnVrmBvIaqXo9Pr0BkNcSyKEYTAaYUsHy5K4XoDWGUJAIQjQ2tBsNkniFMeSxGmGwJCmhlRplDY5dpimBIFPUJjsMi8iK8dpkeg0zXW4hcAkCiFtLG3n3oWuQpzwpB3LRWmVy3kCmcqLt9bgFhwSpXKXJQG+60AqcL3JWGOlXMG1c3/QQW+AJ22KhQLGaO7dvUvVsQi8AgKbQ9FCpYqXXnrpQ2rp4sICxSDAtWx29/Z4td/jzPoGxYKH5xh0UGNrv0NzODEN6jN19vb3uPzIRXZ3tnjqqafptjscH7e4d+8OQeBTqZTY3tkmjiMWFuY5Pm7xzrV3qZTLCCGYnZ1la2sLLwiYK81SmZujUqqyvfWQR69c5t7dLXYOJxvyjtMW79/ZRZYyIkcRqZTG+Sm8t3ZIOzEiTbAdB2UUKo2IegNQklGaolQKjk+hXiIyMVGSMDU9T7UxxcO9B/Qftklf6KGGDnvxZGpfo1gnVgKp4eDwgOPjDgc7BbZu3uDGW29w3Brw1Mev4lZKjMgYqZg4HHNmeZU1VknTlFK5zNE4ZFrXEY6PSjUajbJgd+8QicQ7BROPxuOcHilzxlWWJhSLJVScUCwUefKpx3n9zWsUAo8vfuFFpqarvPnKy9y+exffc7m7vcO97TZ+UOLM6hrt9hGuZVEtl5HGIxwadJoSjSYjCh/ER1rEqzJGZRl+oXBiXpoRJwmhMQxGhtmpEibt0jmMaR08JIwjdncf4DoO8eg4F55PU2amGrz60g0SVSPSFZp7u6AE586d55mnLqNP0XS3yhDULdLQkI0UkQiRdhG7nVBKEqrFMqltUxAWj1RnePLMBdakZjwcUiDjfKGI76yyX36Mf37j27zZ7KIKGr/mEDkp6YhcS/yUw9Ab+9TjMu2tfR4/V+Lw6JCSN8bxNUo5aBki7BGO5SCUxhEppQBKRQdDhrA0g1E3t0+zXDLLorgww+rCNNNT07z9k1eZmS6xWD9Fl1jkZsp5B52xsrKMF/hUy2WMgNZhkyTNQIDn2bk+toJ33r3Jzu4e0clv+AEmHGcZmfqAaClI0gzHcU71+hyPRgRBcCJfq5EnQ00pBGmcgXEwKsMVBmGL3E0os8hOioVWefdiWSCFTSoUSBuTKuJ0jGUKGCXJToHbSqUStpS40mbn/n1qhRJCGVpHR2RRzHCUYEmLaqVKrVpna2sPlcX5hyIIGI9G9Pt94jCk6Acoo7l/9y7SkiwszlOcznWxN1ZXJuZRqVXZWF/nU5/6JN/87d9mZ3uHYlBgf3+PMBxx6fIFbt68iWXZOI5NsVjM/Ro9jzdef525uTlmZ+bZ3NzC933eeO11yqUK62tn2Nk/4NLTV9m8v3sqE/bdo3cZuzGzj82Q2JIwivGmbOYfnWX4zohy4DO/vEIQ+BwfbNPLIuLMoLFwfR+jBTLLKPkOw/GYKNnHsiRrSyu0W12kbRj3Iva2u/Af/vw8mq2EfjIkMR2ksRBK4tou0qQoneE0phkJiztbD9Eo4iyljEV43GO6VKZ1dAAmY6wzxrsZmWNRWJxHWhZGQDlLQWviweSvq30CgTiOQJEQBKUTUoNDlGY8+dgFuv1jtE4oBD62DUHR5xv/6g959LHHWNo4y59de0gpdfEsmyxK0GlGrVKj34+wLQtHSKJTTFQ+zOev9Fd/TdEcGYzKccPD5oAkU4yjlERpus0jXn/1ZaYbRbI0JU0SlDZMz8/hW+CQkQnDYDymmWkC4VMsW6zMzfPYhQsEpTK/+LnPM9UonWoDZrkSQgtaGpVl6DQjMYeYxKEY1FjrHmJsyQuOyxOpIGp2+Fxrn/6ozZ60WQsj5ucX0ctf5szt7/NHWY3f2+qzayksobGqEiMt9GjydahYrnDcGeI7NuOxouCW6PQj6oEE6XBwcESt0qZ4tk467rK8VKdeDZiqV2hM1ZifXyKOxigdEsZD4ixlYWUFNerR3N4jyzIqFQ/XmzyoqVQqpEmMFIKza2uUyhU0+WYjQhKHCe12G200uQmHZDSOeLC5Ra8/QJ9wpW0pSdIMAdhWrqutjSFNUgaDAY49eYCW6h4yzaEfWwik5SGFwWDI0hSwMVoSKBulIBqm2DJAWSmI3IvSaE2SajzfI04VGA+0whKGJB4gpY3JJsMplsw10W1p0zw4Yml2jiyOCQdDTJqxf3CA5wdMTU9TODF8qFQqHB0dIoWFbTlEYZTzua3ccCNLEsI0ZahaPDW7wsbqEtPV4sQ8Nrd3ONg/4L13rnFueYnbt24jBBSLAYtL69y+fYNHLl3AdTxu3LjNzZs3uXD+Mt3ugBc++SmiOOL99+8jhWR+YZ5qrYbQhigMmZlqoLOE8XB46uwmLaSoeoxwJGGWYPk2WigWL88SxQnTuoEwCgvN7OwMcTRkPBphWQ7lUgPXdsnCPqCoV8ooYXPn1k2OdgSF4jRryRzTRcFcZXLxbLZDhMyX9IzJkNhkmQETI2xDGo3o9jpop4oQmpIfcGlhATU1ZMr1WGkUseoleirFrSziew1WLyhUmhFHY8q1Cr12mzvXrk/M4wMryXGY+3ZKaaG1pl6vMlUv8+RTj6Etm2vX76GNPuHlW8zNzlOt1nj6yiWM9pHS5+71hNbBHp1WB98JTob6FpYQeKfoq38QH2kR70aKOM4IQ83+cZhjPkYhjcaxfZ54+lNYQjE7PcfRwR77+1uEkcKSKUnSxfFK+EEJI+DM+adZWVqjVpthuj6F5bp4rkeWGsgS7ODnD2vKjQIFY6OVZLDbIlMWjmMYjXcp1qYpd9okQZHzUvKLIVxvbjNvT7EXtxlaPa4pwc7hJqF3jyk0X5cOHzMB/wcxL6ca2TfYJQurMnmiWCnkDAMLj2hoUZ1dZPncGlMLixweZghxyBs/u0E8XGV9Y4ZaySUKY4RJmJ2usrgwTbFYZH93h0SFDLtjwmaLZLRJFqfMzxYoVgzdfnNiHtKyKBSKjIZDxnFEezCmVPApFQt4nsva2iqLi4tIKfB8D9d1iaKEWrVGp9vPt1NPOm6lc8qlFGCEQJIPeI3JjZQnRVDIMDrNV+SNQZoUIQwqy5A2qMwCIdGZxGgLKxVkZogsK2xbYLRASE2SJRgijACtYpTWCJmhSVGZ5jRzd6E0dmAjEbjSxnM9hMpQ4QjPkvSHI+wwpFYpYglNpVKi1x/S6w/RWiDkNsPhkCQc47ouSaaJ4hRlBPEo5OjgiNkrU4jaZNu8YrlO2O9StB0Cv4Q/57O3t8fi4iJBweXs+gKdTo96fZ6ZmWl2dvYYDoe0jzsEXoGp6Tq1WszxcYtms0maZEzXG1hS0piZYntvm1qtzn6zNTEPC0W16jGMYrJUIXQKtoNXtnGmbMK9lJ3t+wSey/ziQu7qnvXwgxKVUpX67AzDzgGHuw9JOi3ml1dZmJ3mwd0bdKIhF1oNFqYWsIPJV1eBQBiwzAkDCoM2KnfxsXKG2Mb6BtOLS/Tax9QDnwtrqzy8e4fe8TFlBPF2k8VqjWShzGDwANdxabeP6fR7CNdmHEWE6eSrvGVZuK77IWynVIYlBbaVs7rq9Rq2bTMajTAn7l6VcpVnn30e25LM1Ur8B1/6BRAu4xcu8eZzz3H9+rs8ePCAgu3QP06J4gRnQg37y/GRFvGi1tR9g191eX59OfeJNCH9UYvZ6hTil/4zvv/yy3zvB3+MyVL+i7/zHzMawf/9f/1jHr30GP/13/sNHM/DSPi93/tdXvvZe3z5y1+hOr1Abzhmt9mhXK4hjMPK0s/Po+TCqJNSrRQhM6hhRqYtQl8Qb13jXy6fo3C0ySsm45XhgB37Ar9QgrNJwkvhkJmozLPD+xx2/yk6HNAzcFdYXM18fk1o/l6cYUaaRE/ugF84t4TEoVCscxDDMFY4cZHXXr1FcxRSLTkMhg7X7myxtlxi+9ZNxt4ch8cDGuWA3vEezf38JnP9+ibj/n0aRQvbsonVGMv3GfRTbD25A/ZcF1Gt4/kFvvPHLzPo9XjiyjlWlldJsoRGo4DjecRpyru37hNFEQjD1t4BGkGcZoDAtsBzHLQxaKWRMmeWpJkiUxlFfzImPh4eEIUxfuCDkaRZjOfaGGGIkgzHDigERUJhSFMoyCq+5eS0VK2xpYsdOJg4xbJTstRQKBSRQpClGVEcYTkW8SksGddxsRB4Tq4Bc/fePYIgoFKpIISg2WpS9F3u3x2TKo3nWBwOR5TLlZwq1u7kQ1XLwkgbx7epegX6ozEq02w+eMDq6jKFwuSPfJam6CyHqAp2QLFW4NKVS7zx+isk6ZgXZj7OcNDnTv82haDC0099nDSNcku+OGFzc5PFxVle/OIv88rrr1CbqiOFpNNus7wyTxoO2d9vUa1M/piseTMUHY+g7hKpEZZlY7sWwjXcOTjkzR+/S6Pu02z32Wu1cKTAtjTlUgWVxoTDEbGyyewyh9ub+K6LIwUCizQa87lPfhaTKVJ1yjn1PLTWpElCtVpFCYHQhiCNKRjBM3/j01jlCll/zIXGIl6S4AqbT37ucxQLRXauvccb3/4jkmafL/9Hv47pNGk93CFKR9zb3aT5YAedKpSZfD56vT7Ly0snLj0SsJA4jEeaTjvhe3/0E37wp3+M45YJo5Ao9liYX6HbGXLjvff41jf/DZ/95V9k49xF3GKDeJgSDxIun7tM3G0zo88y25hjZmFCEftL8dGyUxoWKu1R8CHOMspOwO0b19na2ePFz34ezy1zsL9Hq7nLTGMKkxlWFheQKJbm56mWC2RaoxH87V/9TxmHKbYf0A0F7YFEdTVOa0SmMlaWln9uHklbM10rEgRlBHsIYRiHGlfC0Dek3SYzSF6UkkOvQCV+iOh6zAnN37FsDrThZybiVpLyJZ3wmJD8QNv80Ix5U2saFgw43Qj3wftHLK7MUZ8SvPHjV+iHYy6vLyELHo+cXWF40OeRT1ymfXTAvWtvMZiqUVi2iVJD4AvSdo8oSugPR3Ra+9QLEb5TIE0V0rXoRpJuXxOGo4l5RHHCYDDEsiyUUghpMQ5jXnv7GlGYcPniOvPzM+zsHfFgc5s4iXINEpMvNklxokPDBxZmCiEFYOc2o8Zg2w6lyuT1/2qlQcFP8TyXcarwKCKEwXFsCsqgUoFjOfiWheXYFGSBOM5Qlo/jCDAW2lg4dr6x4TgWUuScdaM1xaB8snZ+CrXPzs2ZbSGZXZjn+ltvEUYhBsNgMCBLE6ozdaLhgP5wTLfToVqrYknJaDT60KItU5pqvQzSZjAan1jYnZhMnzzrSZElIbVyEVsZhr0+3WEX6VtcvHKZxflpxsM2C/OLZBk83Nxn2ItZXK6ztLLAcXuMTUK5WGBr6z5nL5zFcyps3rmHFprxcEAajUnShEJx8u+y0ThDtVDBtT0SR2NbHplShHpM8Og6b3zrBmGUkmQapQWGDCM1w0Ef1w7wU5/d3YdkBtxyBWU0g2EPy7WRwrBUuUzBKcMp8EF9ZjZ/tsJgRD7/cLShGHt4meLaa28wtbxEvVCkWu2TjEOyMnRlSs8tkBhFWnJQwxjv8JinZpfZTDQhhs2DQ4QoYGvQpwzVnnnqKZZXVpC2xfb2PnGk8bwCaZxL4P305bfZ3m4xv+ARRTG27RJFIfNLcxx3mnjOImfWz1GsVIhJsS0bx3WRQrK+tkGr4FCt1GlMT953+SA+0iI+6vd4+bUfMU6OiIYj5qarxKOUxdlzxKGLsQRQZW3tCr/+a7/OhY3L/NY3/imjqM/m9hZ7+8eUyjUybeiEMaNY0w/bDOOMNDHUSgUsmZ6m50M4VBQqmqIlcP2TBRIDcaYpT3nc6MWsG4MnJCtFyawTQaJQVpFC4HLOFNhNY+6Q8mSsOWPDsh6zrC2mLFBa8zNAnVLEHVJcC4SOmVko4sWSet1BlkogQ/qjIYXRMb7UdJTH+7s9yvqAUZSAkLieJE0ztBLouIkWA7St0SZFSc3U8jwqdig1zk3Ow3Gp1+vEcYzRmjBJGIYRs9MN9g6a3HuwzWA45rB5zHgconR64v8oEULnW5U699AUIjf31VqjVYYREmEpxqNRvqY8IcrFMroosG0HEcYImX8IbdsCDIEq4qcWrrHxXAcyRcvqkDkS27LQWqIzg+vkzBIjJMakuUem5YDIfVKVPqV4KsXmvZtkScr5ixex5In3q+ezt7/HoHuM59j41QpJloHR+Cf6I57nYUy+iZekGcoIjDqBU7QhThJKboC0rBMxrp8flmUxGI+oF0pcv3eb1TNnefv6DS6cW+cLX/g43/n2tzBZxsryGutnLvLWW+/Qah1z9dln2Vi3eePVV+i22wzHfbQjGKmUz372M7z92us0W0esrJ7hzsNr6FOex4XV53Gkg2t7CL+E0SLXDLIkaibjd8vfpNPcYxiluIUiaRTiCkWsDNgFDrpdjns9vvq3/jbf+fa3abaPUWlCuVqGJMbKCjhumdOYAI++8OmTvQyNIsNCkfX6hDtH0B/Tbh3R39tFCYvEbFKTNtHuDmvn1jnoHtMf9PEwrNZn2PzxT2jGGXvjCKEt1lbWOByNUWkG2eQX91de/BK27+EELn/Y/hOiE4xcFyRJkjJKDJZdZjzKfU6PDps4ruCwtc/OwTZbD3epTk/z3HPP4/ounueRnRg112YWeHh0zOFBl532KfSlk/hIi7htF/H9Ke4/vMfSzDS2KfP0x55ktrFGOBTEMuPq05/h0cvP0KitMRhKDo52mZ1fpjqzxuZ+SNB1MEKyfTxgmCgyJLbjEbg2liOwBEgx+VombEUSZ5yZn+Vwuc+dW1s4jkUcaqIQXhKKR4XmbBpjR2C7HtqpIJwSxnLRWjJ3fJ8LWcifGMG+MdgCvig094zm4fQ0pnWEOUVoyfddpsoOhYLFs8+vcxxrnDRhnBoG/S61+SqbR3tE3YTAKxAlMXv3NhGWS5qkIHPT5iwTvHCxRLcT4gUVpisVHnYOiJMIx9PMLi1OzCMIfLqdLv1+P5dXRTIYjpmdqnDp3Blefes9wjAmSVOMUiAMSuVUQylyKVClczFelSkc28rNk1W+tOC5LuVy8aQYT/hdhJVj1jLv7j/o9LUyYGnsRFIcWjjGwfNdtIkRpEjLJlOKLDM5rVGpE3Nhk+PyloPAZhynIGW+XTkhhsMhr770MuFozMLSIh+7epU0TUnTFNt1ON7fxbEtZqem6fX72FIQBAGWZREEAb1evqRRrdYYjkYI6aCUIdOaJEmoVudzU+pT+Mi1ahUJZGlGKA33trYpF0tce+82ye/8Gy5dOMtMvUqpEHDtnfeYmZ3mvffeYevhFmdW15ifmWZ+dor3bvXptDvMzq5y69YdgkKB7Xf2eGJphU9/+gV+9KMfT8yjWlvPlT4BKT2kZdAm77rdADbOX+Rar4tQIXaxSNCocvBwm24yYKAsZuYX+fqvfZWv/crf5M9fepmjnYf4jqRcazBMYzKVkpgIoyYX8cb8HK4QWFqhoxAtFbLWoOcUSMcRxSsbKCmpl2vMVWocb+8yVS1jSkUeW3uOSr1KpVSkXAiQKAo7B1wq1wkadcZG8Qff/T47e/ukp9yQAi/gp6+8wkHrkMOjPq5dwXYshJQMhkNUltFpH9GSmm/8zm8xGg347//+P+Dh5i7f+MY32dvdo93ucO7ceRbnF1FGMRgNGI6HvPjiFzlsNRkPBxT8v5rdw0daxLW0OXvmEYzWrK6epVGexbMsoswmTVNAUQhmKBUEo5EhRbOz0+H8I5/m8lMvsjfMGB1u5ZxmPFIhcVwHz5JYJ1d527FOpUwZI+m1Uw72ezzz8ce5e3vnA0VZWsdjXtcSS9p8UWueHKVUhE0SuNiZQcZdullCMxkitOK6gD/RILXmE41pjmanYH2B7Ic/xDqliFenfZxAIIICjDsszy9iSUiQ9LohrVYH14PD8ZDecZc0NSS2hbJS4gQsoQg8m3rR4rlPPkuz2abTHxEbWHnkIr3OkEq5wP3rtybm0el0aR8fE6cpnu+cdLUQhjFnV2uUSgXanX4uwWoM8sSpXUqJlAYLicxEDp1gSLOTl0BIEOB7DvVajXJ5MvYqjERnGikFUubqcLmRt0aZjDRTRKlCohgPR4DGqngkQJLFGJOzQYSRZGnODbcsG2P4cOCqjcaSk499oVCgXCrR7/b405/8KU9ceZT1s2dxPY/haPRhQc/lQ9t4rpvfSshxbMuyaNQbICWHrS7KKDKtyZQiSWLOnTtH4PuMxpMpZFop/GKRVneflQvrPLz1kMNmC2E0u7v7lAslht0+6ATHtYnjMZ7nE0cRzdZRLi+sNJcvXWG/e4yQLvvbu7jSQiPZ2jnkH/4v/yvH7eOJeSgNGJHz8VWILQUYjTIGhMQpFukNh5SnZvi7/+1/x9mzK7z37g16vS7lSpUrlx5laWUFYUnOnn+E7fv3KDg+whiyNCPTilQr5CkeNbaVD7xVOGbv5l3qtWo+g9hp4hcCpheWqdQblItVHMdhlCoKtSppHDO3dpZKo4o2ilhCmIYwU8UuFpCuoIjLhfUzDMMx4SkV5Pi4wzvvXOfajevYTgGBh2ULpqbq1Gp1dnYekmVjFuZneOWlnxCGIf/8n/0/pFlG86iDbTu8++67fPe73+WTn3yed959h2vvXQPgR3/6A+bLdY77PR7sTl7C+vC5/JX+6q8pRpHC8RqsnXkaXJHLv+oMpWOQMB5laKUQloORgqh3zPpjn8cv1ri31UWJBHHC8S04EkdaOAjQMWlmGI4NJvApeZMHaAU/oFypcNzr8fynPkZ9pkK/1Uda+aG/g2AIbErBVzLD15IRrnMAVoCwHUhGbErJvsp4shDwM9/lpVFI5RPPI1fmGN9/F2FbCGvyYag1agyUYXtnm3oF+q0jAPxylY21ZTxLUy4USXpj0lHGKIRmP2acJmihKQeG+aLDk5fmGMeG2NF6NQAAIABJREFUmZkFisU2W70WQcVlbu4ch/e3SNqTNzajKMQAtm1Tr5ZptQekmWIcJnS6PR45t8Yb79wkjBIQkCYaz5FIkcMMgpyWZ7TGfPgiGozWCEtSKPhUqxVcbzIW7bkexlhkOqcoupZNmiYny6+5xkfqpIzMGK0VcZyCVcKYnKlg2TlDQBobYXJc3miD1gYpJJ5joY3C6MnF05aSjz37cYQtOWo2uX3rFlprNjY2EECxVidCsrd9QKsXkmUanAFJkpBmGXEU57cOyyWOIzS5YFKSjBEWnD13FmMM7ilaMkEQoI3m/IULHHc6WLYkKJewrXyTdTgcYZmEajnAcRzG4zEbG+ukSvHKq6+zMDVFtVxha3MLv1bm4cMHtA4OWFte4cIjjxJicF2Pz3/+cxPzSOMYcfIMhS0RSiBPSodUgum5eaIsxck0a2sbnF8/w9n1i6gsBQRC5wtfaZZy8eJl/vR7f0RQKBGOQ5IkwbZdjMkFuSbFaDzCAaqlAmOdslitAIK5M2sYKZidmWd3a5vv/8W/ox8OyUyGCUdYaUbwL3yM0WiT3ySlPGkWhMQyFiDpC/Cmpll/4qmJeXz723+IVobZmXmMsNjbPURaiitX1rl69Sp/8Ac7DPsp1XKBo31No9rgBz/8wUnjI2g0ZoiikH/yT/4Jne4xb739OpsP38d2HL7z7d/nV7/0NaJ4xN3Dfw/1xLdaA1AaW1g0iFFGI4WDEg5xllCslkmimHu7LfabTVTYxa/OI21BxVfMNBbwvIAkSZCuSybIl1SMoBx4LC3MUCkGTDUmF/FOt0+3PaZULfK91/4cf9EjVQXC3hgpJMlqmXZfnwyzNE9Ghu7f/S/57T/9Hk5jkcraGv/y336DdiqwPBvHsXCGGXf//EdMryxyP+1R2HBIDyYfyu+/eptYG7787Fm2DmNUdEiSRixNTXHrtdsYJ2V5dZ7PvrDB8V6TKLPYS+DB/WPWG3Ms1AMOjtvc3RnxyHmItCKWkrW5GfYOW3TLmmzYZf3MZGEhz3NxHIssUzyyNkch8Lh9f5dme0CabbKxOssLVy/xYGuPvf0miZKMwhjrpCOTIl820Wn2Ic6rtclx08ywsjiH7zocHe5PzMN3cuglzjRSKoRIcO0chpCWjbRS7KKNcArYlsARmjRNKDkOynPRWlGu1DBGMBgeYUhJkxRjBIWCT5oooihFylO0MeKYSq3KV77+NUqFIq/89C/4i5/+lBvvvYeUksWlJTrtNtpyKVRrhOOQLDtZaPJckiRGSIHnuZRKAcKSZFrz/Kc+wcVLlxFSEkXRye3z58fh4SFxHOc3g2KRM8vTFFyXJI6olCocH21zc7/JY48+QqkScPbsKr/3ze+wsLZKZxwRxkfst/pESYJxJCsLszz7zHNsPdxhen6O3/yf/yd+9IPvcZpJhtQGlWYYBI62MVKgpcxNM7Tiscceoz41xzCKOTjYZ3FpgSzMP/gYQy4ylPOrX3j+E/zO/zvLa9feZW15IYffpJX/+2by++IYh+PDPV56920GzT2uX38jF/DSQJqhhxGOVtQLDnWd5Y2gViRpQtg6Ih6N0ZlCGHCEQEqB757c3KXF/GNPsPHoU6SnbBb/8uc/x/7RIT/88R9Tn5nluHmI7Qju3n6H9++8w+rqCv/5r/8qnXYf13isb1zg97//b1manWVxcZEXv/BVfv/3/x0/+9lr/KtvfoPd3R2kZVErlvjCJ5/jE889geW5TL+3MzGPD+IjLeKt4w4SQ+B5qNQQOAbPdikagzaC1nELB4VUQ1TcQ6cRpcoctZLH6lyJRjXAkhKBjV8sIWyHNMtwbZvAt6mWCzlf05t8KG1p01hooFNNuzUiizK8skvcD5HSwg9sQqVwhc1YxVxzCqQvfgFLJhRrDdJSEfPHVUq7e3ieRdBPGSYZO/GQze0dtA9WWSAnowfMLARUXZ+d5gFufYYHmx2mZ0po3+X1G1tMT4EduGAyRlnIIAwZ9y3KNYfd/Q7bD+7jlcvgVYniMb5bo90OscwQxzO4bpHK4gKFwmni8rkmievmMESt6DJdK7Fz2KaQCDwv3zqrlDzavkMW5p2s0uZDPFyQDwSV/v911Q15h+57Dp7vM105Zdpucm0WdAIyxbZyLW9tLBAWaZYQZykyMniuQ7VeoN+P0EmMa7kkCqJBhDKaYsHHD0qE0ZjhcIRjG9I4xZKcis0bk/+/4jimUa3x+OOPMx6POTo6wnVdlFJEUYTBcGZjHddxuXnzVi7kFuadd6oUWTimXq8RFIucu3CeMxsbCMum1887rNNkGYSUTDWmMErRP+5y9VMfZ2F+Opc8SDQ6Vdy5s8nFi+cZjo65ees9gqBIs9VG2xZRomm1DvH8gK/8za+QDDvUSlXu3LnHV7/+daZn5vj8L32ef/Sbvzn5eZz8vpbMremEY9Hp9fiL196m3elRLwcUq3WG0SH9Xg8jLKRzUloMhCo3UPFsF9/L8IIidlBiafUMneMDpBAoZU4t4vWy5OzyI3zmk4/zzuuv4WATj1NGgwGtgyPS4ZgkHDHstGh3ugxHI6IkJlUZjm3nksGZQgI2ucZPJAxSgOPbLM8tUalNM7An148nLy9RL2na+yuMMounL19gNB7gB7lJ0sxMjeOjhxhtWJwP2N2+jlEx040ytlC0Dg9oHe5TKto89/QT/Ljbpt8fMF+vUin7ZFGHOFKU7VNWz0/iIy3iw9GIwLHRaYKyNGM0lu1SCGxSpRl0WoS9XcJxh8xILH+KernE0kyJhSmPsm+BUniuTWOmjBaSJMutplwhcbKcr6z0ZGxtqlHjwvoCnU6fYTdlMBizsbHMQ2HT2e2xuLjB9tYO42HEIDQ8OFNnetgi2dnixo1r+EIzdXjEY5nmRiI5FpqwVsXYDiRjzDABaSGDycXCKhkW5osIb44Hd7bx/AKlUpXjzhDHKxEEFq1mgi1ytxQ/sFmfW+X6ew8IhwrhWsQDzaxn6HXHDENJrz3CNhq3otk4t0zodClPZpAx+EDa1LGp1qZIU0WjFtLuDnAdn/v3d1lZWaRSmSIoDBiG/fyFMLlGihQylxkQMocrPhhIGsNUvcSZM2dJ0oTkFH62TnJ6msgysjhDuhJhgeU4ea+YSVSiUCrEAZJxik5zNhBGoVRGEufMHdsyhColTWOMiolCReCVkSKfvZwWvu8TBAFplhEUCly9epWbN29ydHTEoN/HcRw2zp/n6tWrVGs1alNT7O3tsb29jeO5FIKAcqXK8toa8wsLNGanyZQmSlU+W5Dy1CJeKVVp1KscHR0hHcHrb1/jsSuPsLK0yKDbw9IuX/zSl3m4/RA7KVEszVKczWj2B0zPznOwvUs/Dnn60kUatQKlxQbf/e4PePFrX+WZ555HYDEzPcdXv/K1iXn0+yOKgY9AobE4PDjid//1t/jBj1+hVCoThSFFxiRK8ZO/eIWjXoxFrk6aZorD4x4qUyzOz/OZT15lemaeK48KKtUanXbzxMVGnSpT0Zir4roOqdKsP34F6Xloy843gvt9Oq1jBoMeg16bUqdNr9sjGo1zxUGtMBjC0RiVpPkQ3ghiy8JyJCYISJfnaKGJo1OWfeyYtbUplpa+RLs3Jooi2sfHGJHf8lzXJYljkjShWlmg3vLISNAqJuyPePknPyAeNJkquZh4wOMXz+I4DmurKywuNtBqjNYpF+cn3wg+iI+0iGfJgFEYkcVDRDZEklO/HMsiNfn1eHC8hU4HYPkU/RKFQuHEeFahPSenlgnQ4RjLsvCQZB+4cch8iDWKNJN6vnqpyEKlilCwOFXEDlweWV8hTaDbGZJoD2NDbBl2bJs3Ol0+cfdN0u0Djm/eoKBSzDjkDduiI0HVp5CWQI761Kcr9EcDUpFRqEzeUMxIGKiMIHOxrCKVmotlB8T9NufXF1iYr3O4f4QjPI57bVQrY2a1xaMb0wyVZj/scrA5plYu0mmOiOUYI1O63YS67aOiDGmKFE8ZKPp+gNFg2YJKpUoYRszNGAaDMb1BiMkyHm7vIiXY0sJ1LNLMJknyjtyInBP9Afas0CidGyqvLM3h+z6u65zaaRnjkCRh7pSjHVRqoVJwDSBzjFuInGImbFBGg8z51io72cy08oFoko6R2nyodmgMSAy2FZwKY3ieh31iYKGUwihFuVzm0qVLFItF7t25y9z8HE9fvUpjehppSz7z2V+g0+6wvZ1vaxYKBeq1GsVKhVRlJCrDCHCFhW3bZJk6VU3RaMP+/gGu6+AFHo4bcOPW++zs7PDpF15gf/OY1954i2brkNZRk9F4TM/kUONRq8ni2hILizN4vsutm++ysrbKuUcusHHxAg+2t1hePsto0ObCxUcm5nHn/Yc8eeUimc4Y9iK+84c/5PCox9/6+pd59unH+R//0f/Gzt4xRid8+/t/wvf+7B2EkCgMSmu0EghjsG3By2+8Sdwb8dxzV+m1u0TJ+IRRlDOdJsW1O3dO1u4hThKEJTHkUE2m1EnXnaKLHn5hHm9uBkfnnPwPaK9Cm5NNXIvcV1EQ64RYK8axRrd7uP7k91ap4cm2aEbJiyl6sDy3hGM7xEmC7/vEWUZ6Mvy3pMXfGEQnN7iYVrNLkj6DbVsUAp9Go4FlWWilqNbKaJ0SOAFV/9/Dtfs0PiLqN4nHXaQa4lgi5xFjgeVRcCVG9zEmQmeaOBwQZymZsgljjSCmXAzwXSd3brEt/j/23jzGsuy+7/ucc+769qX2pav37umepWchZzia4S7SpKghZVqWGCcMEsERjECGDCgx4ABJkDgBZMABkkgJECAw4CRWJErWQpGSuJikhjOcjbP29PRe1bUvr169/e7n5I/7pjVWMlWU4QwsoH9AoVFdqKpT7937u+f8fr/v52swZFrQTTMsk+/KekHKad6fiDYKArIMqgWP08dPMlVvUi1VOZhL2JlvsXr9GnYhw0iLftHhRppQffVFoplJ9raqjAYD4rrIAUtFl2LFIW33GB70KJZsSvUS3W6f6anD2RiFzMdSVTbvDDDaplRuMBhG9A8y5heKOMpCRzFxYIgSzcZazMQDA4Y7HdY3EnpZgm8VwcrYbg1wagVOnZ/h5uUVpGvRGfQYdiRu7XA6XL1WR0oFQuN6DsqSeI7FpQfOc315g+3tXdIgQSmN79hUiz5hlOau5pnOBT5jfoo2oI3AcS3mZyeZqFfZ2NzA93yq1erh14eJSEmwLEEWg5KQpik6zrA9AcrCLViYTOP4FkJBHIakJskTosqNObSIyUyCa1m5W8zIxvVLREGW26mpw5OFVBJtTN57ASzyMcparUapVKJUKNJsNqnWqmg0Wuelhlq9RqlSJhuDi4w2hHFElts+gTGo99i5HRWdTgfbVoRhQKNRJY5idJqytzfgD//oj1maO8nKyy/iuDae4+UnEglapyA1rdY2g06fY7PztLZDRmFe115f30BIl53dNq2dDcrlEqdPPvD+r4eAIBjheBbSZDz9kUf55MefYmZ6mka5wJMfepBv/sEyxkgcW2J0yCBz87UYg8pilBDEMfzopTf421/4KB//9Cd54c9fRgiIoij3r7UOT57tgxDH87Acm1R4KA2KcfnLaGyjkCIlMxHaZAgFtmNhSYmrbBzLRimFkpKC5wMWCIvEZIRJzGAUE8UJ6REntSDqo5SF49qUfZ8kztHPmARMnBtCKDt3w0KjpKRZLeUbgkqRuakGlmWjM41QuQVhFOUG7JYj0drNlbU/mSfEB5zEu28TxQGDeARBH2Ey4jRFWhZRZvCUxLMKOLaPX6jQmD5BqjOi1ID0SVH0I0lsBJudmDjLLasyA0YplJXiOA62dcQxtehz0O+zvrPHRx96lFPTs/RHCRs7u5w6NYV9Y5+ttTZplGIVHXbrDs+/vkzBERQLmv3dDlmWo05PHmtS8n2cY3WieJY7y5skWUqx6iLCwy/KWuMC165v0dmLmJ8osDNqUyqWsOwKg0HKzRsrSATxKKHs13n4jKTQi7nT6jI3fZyzrmIUjRjYCXFiEwwTbl5pEYsyt6/3cOe2WJg4wcbBzqHr6PcH2LaFkoJarYptWZSKRY4fP8701DTPvvhjWu0uo1FAlMRIKZmoFhhGMYMgJgzzmqlBoixBvezTrJSYm51kZnaGKEoYDkK2t/Z4+un3d2OI7V0SIiIyvHIpf08xoAWZsHCdIp7nk2lNnAzRWmOV8+aplCZvUIkMYxLINJGJiYYJll0kjCRGBSQ6OsJBEQZBiOtoLKnypp4QCKlQ5C73Z86fH48UCsgMOklhvIHQIp9NT5KEzBjiTOdY3lQzHAxJEgNpjv0Nj/A+9X0f33eJ45DhcEitUOWBSxfo9FokacjBfocHHn6AzbVNRsM+fsGnNjXFIOgzGvbxPJtHP/Yke9sHfPILP89zz3+P/+a//ce8ffUq3//z5/j2d7/LP/zVX2E0Gh26jsXZJsWCS0pGtehSLc2NFbkZ4ajLf/Vrf48Ts3X+l9/8X/nMUx/jH/2j/5y93RbDYIRUikaziV8os9fu8T/+z7/Bk09e4n/7Z1/Dl5Kpupc3w4VGH3EyEUZitCAOU4StxiblYx9WDJZSWEojpA+Yu3z5d42xEwSpEUgtSaJcxQspmdakWhOmMRkarQ9/wBqtCKMkd/lyiyiliaIIy5IUayXSNMW3iggkcRTh+T5RFiPHBjhpGqKFRtoutucTxRFe2c9tHTNNMIgRwsFYR7i5jOODZafYQzwZYRMjLBvPLtIbDhiGCVGSUvJrFItVhFXGL80ghIfveaRAaxjhOy5CGjISpGWBEXed5S2Zc7yTNB67wbx/PHnpIrZj4TsWmYmJE40QFo7tkKQZH3/6YV56/Sp7222iIMVBEvTa9HWGbUsKlQKjfkC1UcToDFspMFku7LAMw26AELCbHr4O6SruP3UGfUJQLSra3X0c2+XAKRBnMDvn4DkWRd/BEuAogXI0xxdmc1ylIxjsh5BKBt1ebj020rSjEaen5ij6Nu+8/TqtIOE/+Or7ryOKIgbDARPN+rheq3LvyDEv+aELZ2gd9Hjn+m36w1HOSlF50jYm9xM1Jm9lTtbLTE/UmWjUWDy2SLlcZvn2HYIgOLKconHy0kOcIJXBFuPhBiGQypCaBGH5eI6LCXLhTJxGaPTYzV4CGp0lYEl0prGli7KLxLEGoXFUgSPuUcIozN32XBeT6bzWr8dqVJF7IOafq1xQpDVxnJCkKVrmDclM56eTLMvGHzovMWQxSZLkJ4wjlJLGGPr9Hp7vIYwgSTI6nR5vvn2ZMIk4e+YUWqfMzk5w62aX4bDPqK1J0gR0RhJFbGxu8omPfppPfPIzrG2s8fd/9dd4863L9AYDjp84zq//k1/HGM3Pf/n9L5CC741xU4YMjUShs9wHUgiI45DBoMPc7BQXzp2FJGJudhKEyEtcWYbOIuYmSjzzmSe4+ubr3Ly+RrOqmJ08M+4NjC0OD4kkTdFC5OhYNCgLneYOULkZSDZW54q75TCdvbvu3K9VGoGU5DXyzCCNwIj8c60NaZoSx4fXxIt2CWMZSn4JoSySaIhODIZcSOZaDkIppJDYwsXxPfrtIUo6+F4ZSYU0zYB3ryVDnGYkqUEnLuAhkBj9k6XnDzSJn5+RQIleXGa9FRGPAlzXwwgXoywac+eplupo44BVwUibIIzpI0kNuCLE81ykZVEtewjDXQl1RP4maa2PnDc9PTtPkMV4joutIIpTsjRjslZholanVK7y2KX72NrZZW+/Q5olWEKxvLKNMTA5WWTH5GrEOA5Z3ehz7swxLCkYDkNIDV7Fyrv5h4Tnujxy4X4GwxHJqE+h4mIJG9+O2dw/oFIvMFGrIW1JlEWYzHDQalNrNikVHKQCUbRot3sEu1AqenR7fUqei+sIbG1z48YagyNOBIZcql6r1sAIBBJLifGNkTLRrFOv16mWiuy09rl9ZwOdxcRpgBQC13EhTihWHD586QFKpZx+WK7kF2uWpYyC0dHI09QgpANZhk4t4gyUspFKksYpyskVoWkWAzJnUsRB7mmq83JOlmW5e7u0ITEUi2UalUXIHIwJcdzCkYreKIpyJozJKzRY1t0E8W4iV0r9awk9SRLiJEELkJbKZ8NNnhTe9dzUOm/ehVFEGIZE4eE78TRNiaIot4uzLEaDgJWVO5w5c4bN3U12WntkUUKtUGRpaZEojtkaHpAmKb7joLOU02eO8eLLP+LWjV2+/4Pv0u/3MUjKlWr+90hFfEQjz/FcjBAo4yAwuXpz3Jw1aAaDkJ2dDg88dImf+dnP4xYKJGkK5PAuYUTex8piiCP8QoVz504wVTHMz0zkWOEowTtCSTscjnDG9z9SkIic666UGmOTxV0zbp0x3jTkvxuRW/tlxqBMrjhVSNIkwchcyBRFEWb8vh0WklzEFgcxsQ7GGxSN7/q4jkeWZQyHo7sbzCiJUcpCjZW7eSM+f+ikWhOFmsQINBaW8NDk5TzxE6ZncdTu6F7ci3txL+7Fv7vxk4nz78W9uBf34l78Oxn3kvi9uBf34l78NY57Sfxe3It7cS/+Gse9JH4v7sW9uBd/jeNeEr8X9+Je3Iu/xvGBjhj+1h/+kbGUi1K5cEDIHPyfGUOqyeXQxpDFCXEckSQhV6+/wMbaFYLeOirrI7UBY0Fm5eNAYjy2ZkAYTWgUBdvwzT9be1/Fz1c/9YRZOHaSZmOS73znR9gFn/kTZ6hONZibKuPokI29HqJQJpWCrZUVVnf2malUyMIQ4znIeIQSkp2DAd1ul5miz6RbpiOBLGN9f4PQZLxx5fr7ruNLn/uY+amnHmNv/4AvfO7LJIMdTpxcZKeTsL96nZ/+936F7/zx73Pp4lna2xs4KP781Vf40t/6Bdp7G2ztbfPySy+yubtPvVyi1+1RrdZIjOFHL77A+bMXkNKwvr7B7/3+d993HbPnzpinPvQoP/+lL1IqVXjj6tv8yz/8Q1bX1nJZuxEoBEoIlJBYloUjFbZrY1mSxYV57jt3lnIxVzIW/AJiPNJVLpcRUrC6ukIcx/zDX/sv3ncd9519wGRZRqFQQFqKbrfLaDhCIPA8H60zvLFRcxzHRFFId9AB8tHSfKZcg9aUPZtL919gbnKCTrfL8tY2b1y7jZEWxmjiYPS+63jm0xeM7/s0m016xrC70yEYZYT9AJXE/I0n7mOiWsByQWQxKJtOHNPttdnrthmYBOU5KEuRygQjNGmamx9EqcFVY/u2VPP1/3vjfdfxoYcfMe9Oj0mjSIHpus0TSxVcbYiSjDgK6UTQTwzDOOHvffkpygWPm7fWkFpTqE/x7OtX+f4722ihECZ3YhJCot5DL3zhtdfedx3Pvr1uUt1j/2Ad1zUMWyMGrRFnz9yPWyjkmgEDGs2VG6/wre/9HmQFVtdvUKy4ON4QIboUfAdLl9lvB9ieJAgFwmgWjqsxh93l//gnr7zvOv7gSuvuKJ006RhbMJ4xNyZXfArymXajkUZQdr2xxkLiOxaeo7CVwrbHnB6hAQthBJbOf5SW0PAPBduYd631/r/4N++KjP4q8e73/OXvlUchN/nAnX0slFQ5OCnNXc0d20YJATrLhSMGLGnnZgNKsrD0KMXSFOu3XqG7cwNpjbCkwQT5kL9W+XywMQaTxVhYpPHhf/fWbky7vUKl0sbKApS0mT/exC4VWL55m2j3gCQRZCJi7tQJ7j++RKVUQQpBb2OV6ekJQq2xe7v4zgzbjsIeRiRhSg+Nb0VM18tMlmuHruPCxTP8+NXLPP7EYyzfvMZP/+yXGO7cZOf225y99BFW33mdtRtv0CxoJicbTDYruDebFMoNtjZuMhqN6HS79Ht9lubnqdUq+L7HbqtLlml6vQNsx6ZWO3wdWZbR6XTY29ujUqkzMznNpz/xCb7xzT9lr7Wfz99KifhL46h5IjL0+0M6nR62ymfjkySmVCozMzPH/Pwitq0IgiH7+4ebDzSaNYTIAUJbO9tkWZyzJNJcTOP7Pq7rIoQgfY/IQ+tc0GEQoCUmCfmlX/pFzp89haMkrdYea9stRnHKtdurudT5kOh0XTo9Tbc3JJQJO1sHDAYJMjMsTtUx0iVFoIMRJDFxPMIqlFiYWMASLst7W2SJJtMZo2yINmBQJJFAG0mgc9Wr4xzOxnCVfXfWWBhJlISMdaNstPvERlEv+qRpyiCVbI1iVrfaFP0C6/sjSpbhwQdqzE5dYqX9r1jbHaGUlz/spMRScgwrOzyMSLm1/Davv/UsSdon2E3pbYVcOH+LT376s5SbdYSUxHHM8vJ1guEBrd1N0mxAEAb4RYVAkcWSXqdLtxNSnSghjEWWatJEMwqGxPHh/GzxnoeOQCMQ+QMJUELg2nIMXhNoLZCAIwSWAFcpHCVz5y9jEGMDk5yJn3N1clmHIP/i4a/KYdybv2oCh3H++ksJ/Cf9OR9oEncchzTJTRAyNJ7nMTk5ibIVnqtwHJcsTTFpimPnbINypc7i7BynTz5Au73P9tZVtteuoMUKQqdg8gsx05oMhaUT9BF8ZK/Z4NzcJD+4fJ2nT8yRdvfZeOMlutJje2/IwsnjzE3EbLd3SdzbhDY88aEFil5MkNZonjzP1lafvddSqq0JlDdBoDM2dnapF1IuX7vJE489zvEzjxy6jp3tbZrNGqVSkTDNKFgw0pJUFei2DpiYkUxMTfH4T/8cabCDFD7h3u/yjf/hH2Pm55lYmKXmFOj6EZ1elygKqFarCClxXI9SucxwMEIdwQoRMn8NpZRMT01RLBUIRyO+/MwX2dza5o++8Sc5i0IIpMgdZ7QQxHGKEIL2fof9eofpiSmqtRoTzSa1WpPZmQVKpSrb2+uEYUB0hMy8WW9SLOYy5EqxTJImjEYjpCWJ4ogoCKkUy3iexw6adnsf33GIk2zMOgmYmaryn/39X+OJh87Q7uwThkM6BxEi6PKJpx9neXWV9Agx2NXbO9i2g2XEtwsWAAAgAElEQVT1SdMkl4TrFEvkbOyVrX2W1yPqlQIlW1B0XbY2hkzWFdP1WZr1Gi9ce41AaYq1IrFOMMLC96skQcRuOyFOQow53LmlVCzcVblmIkZEFjqMGQ0iJsouJ5fmmZ+ocmN1i+9fXuOBUyeQlIgSm4PeiONnj6FHMX6c8tjiCZrFgNWdPVAORoA8wuPz3fj61/8nbt2+giFmsTLNwVYPEdgkQY84aCG1R5Am/Mmf/QkvvvAcnq8J+gmNyRpB0qK3n+C7DqNQ4doWZDEiMyRhSBglRGGRaGRjdOHQdQjzrkVcbhjyLglSCoEnDAXPzu0FxzA2tKFo20gpsJXEkmAZg0QjMpk7PwnwpEBIQWo0GoN1xP3ybxb/7weDHgPatNZsbGzwyiuv8Prrr/GVr/wd7rvvcCjZe+ODZafEEa5XpNaosdfaJ44TgiCi4pawLDs3iZASoSSWsikWK2A0URzRG7rYBZdSrUitNsk7L+wj4gFkKVqkuVuHsPPd2BFwocc//nkGlmLm1NNYV/+cudYbVJw51tKY9dTi4iXNRD1DrVgMB0NaHcl26wbKMhiTIt/YpliqcPHMIi9fvsN+weMrX/ll/sX/9Tu88/q3kCJjbX2ZiWPnDl1Ho1Fne7fNxvo6InN45aVXeOiJj1K8s8qDD53inR8/h0wjtl7/Ns3ZBUa9CJwGt7/7PS7+8gXe+vq32I4GJNJiYWGBbvcAnWV0DrqkSTJGy5bGnIv3D5OlIPO9zUFnn2KxSKVc4vr1azQbNTwv59PkPpq5DD3RKUrlR1gjod05IEwSfL9CpTzB7Mw8tVoDrQ1bW5t0u32sIwBHRgqSLEWToaxcqSmkRAuN5TjjnX/KYJAnV6UUBddFD0eEwYipepH/+D/8Mk8+8SDBYAAIsjhl1B9hkNy4dZskTRHiCDqcDrCkwZKaGJHTNmWGIw0yjQkHwdj4wcWuFiDJKXrDUUDRg1LZ5r5TZ7i1u0YKpHGCJkKRIUQGxLieQtmH336uZeU7cQNCCo7Vq1hZSKnic2GpydmFCaqlIsqS7PZjNvZ2uLPcpd6cYHGmSZYGCAlhHNEoKGLtYIs6mwcjUmnlXBrgKMHftZsv0uv2mZ+cob3Vp98dUa9NsbS0wOzMZP53aHj44gX2Nta4efsKmdH0BkM8XyCMAm0zGiXIok2aGVzXR1m5IlXHeqxoPUopaVBjMwpbibE9oERJgaszfNvK+fZjVg3a4Ns2Bo0UBot/PeEJ3k3uOd8hk4yRHZp/6+1CocdVH0GezCVSCm7evs7VK1e5evUqBwcdSqUSM1NT7G5u4PpFqvXDT9HwASfxXq9L0/OZnp1id3cfx3GRUuI6HsqWeb3OWJTdMpOTk7iuy7A/YjgKEMpCqNwjcWLmJNWp07TXryN1H1QGaJRx0ViIIxgMtVqFOImo9zsM7SKeFEw258lKJ5E7z/P8s5e5fWufKM6IYoO0PNIwpjbRRCPQloZ4jx/M9TktoL1jgUlJkyHtQcL5M8dYWdtGvfHyoetwXJt6o0alWMayfMq+y9UXvk0SRQw3r1KoN7H3eki3TKgdWleWMZsbdLsdWlducuW1Nzn5+Y+R7e2zv9/i4KBNqVTCti1s28FowaA/wD0Caek6NgedDjutPWanJ5FS5L6RQvDjV19mfn6W7Z09jMl3LErKHCo0dmuxLIXrOiwuLlKr1pmenqFQKGGMJggCoijEsR1s+/AkLpQAKy+7CZNf6J4jMUaOk69k0OuzvraOEDk21whDnIQ0qjW++pUv8LlPfwJLCpS0aNSbDDod+oMhyvV48cdvgXRQ8vByyrs9GmEMQoI0enwkz9kn/SAEDFGUMAo1wySg6Eo8mWKylDQSnJiYodPbYzsOMaHAGEFiIuIwolwoMBjFpEcwXHylcpYEUFIOF5fmCYdtZqYrNBcm6aQB3XabIE44szTFw/efpLW3QyZycp/jGvpRTJppzizUEKvblAs1wlTTiSTueCdujrhf3GKCm0kKrs1BINBKEmYBjmfT7/VoTFaw0Zw9tkTpZ77E7/zLlNb1l8mEJEs0lmMxHKTEsaAVDQiClPb+gKmZCpVSEZNpLJWRiqNs83IDbikVjj1O4EohhcBKNLbMyZcKgRE2aIMt5fiv0/lDQOQPxJxkmZ8s5ZhxI2w5frn/f1Cxm/yeydEghl6vx52VFX782osctA9oNpssLCxQqVT44bN/zPV33ub+hx7hs5//hSN/9AdrCtHroiyHqVmXmdmpMXMgw7YVyhKoDAoFn6l6g0KhgNaaUrGYMzxySjVJEhEOA6rz9xGlmnB3GZn1sNIoTzKII5/oe61dGrUSq0nM1FQdLe5HUeTkzEOcP32Fr33zNWxlkWQJluNQn6sxs1BmMGrjFiq40yc5WL7NfmIxX92n4p7i2T/5Y15//k9BCSoLZ/F6MZcvv33oOvbbB+y1ulRKJRYmbSw6DAYtVm/e4lOfeJzOxiZFz2H31csc++iHWX/pO7QvX6NcrjB87jUmTp2g3z5gMBjgLS1Sq9VQSjEYjMiyFL/g5ebBR70xQjAcjbhz5w4Xz52l1dqn2+mAyJ3ftcwTtef6VCoVbNumXC4zHA6JowidZUxOTNDrdalUymQ6ZTjqI4QgiiK6ve442R9+uXmuhWWJPHGl4m7Sj6MI2xbYWFhlHzE/jRBgWblB7aP3n+DTn/gI507O5LAlLBqNSQQZtr2O7/tkQcpwGKAc/0hnHz02zdYGHAG2FHjKwncUWRqTZQkGjW2nOFaCq2CyUsCg8aQhC4ZY2mVhcpo7166iUKRJ/hASCIqujcLBHGHYXHTsu/mk7mRYUY/JSom5xZNMLS1hTEo4CrBCjTUcEg4H+JUBaZSh44xMwyBMUEoyN1UlCAKubx7QKPqkWuMIkyNxj8hZloJqpUQQDHFVna5OOegfsL63hVcuMz17Ekcq0izjzJlT/K0v/zxv/9O3kMBoFDEaZRgjEdrl7OmzRGHM5vYq2+t9hCVIRUC9Vj7y4WrZ+e5VCYMl82RuKQtLClxSXCv/uhYSI3IYmhRghECYcf1cGJQEaY3/n7zsojONGFu1/dtEkdxtTEtJmmRsbW9z48Z1rl+7ilKS02dOYam8b9Xr9djZ2WFr7XV8R2GpfwedfTAp25vrWLbNxOTU3Z2jZQmUkviuS71Sw3c8hDbYUmHZAnyXVCe5PZfn4LoOI8pUZu9DJBmD1i1co9Hk/nlHNXTb3R5nz8xTGcT88LUVyj3DJ483mA/fouHZNJoFlBAcdCIcV9BsVpCuYenYPKNByO0ffY84CJlcqJGVyjSa6wSjgKnSDDd2bxFHCcNuC5Me/iZcvPgAV66+Q5rGTEw2aM4cZ2t5hXrZRQy2iaKE4kSV5a//gMQKUQY2b15D94dsxYYTX3mGW29cxzm7SBCMGI1GFItFCsUiruMyCoYMB32K/uEOIZnRKMui0+/xzrVr1KtVWnster0+pXKZ3f0WxxYW0JmhXq9TLBap12rst/cJRgE6y0jiiP29XaIoIMsKRFFGHOfgn7feehOl5JE88ZLjkqZh7tcpNGkY5pRIoXEdB6dUJIptmmUvN0pOMiYaVT77ycc5vtBAmAxXeEjjYNsOg16HUrGC6zj0+z1KnkuQapLkCNToeyYFGgUPRzpIneB7FlEcoIQhSSLm52p8+MHzhK094u6IVGcUChZRnODbisXpWeTbV5COQzy+Ll3Hw1USLQwcAeIqOS45UhUm6xkyGzEzc4KSX0X3Q4wUyNQiDAPCYUSSCOoTUwxaB+y398m0TXGuQqHkEiQhxUoFtjooAVXXwh5TBo+ifpKVKXoV4jjGK7gU0wJetcTa/ibGdXnqiU+QxDGWrUizlJMnjvOLf/MXeP6FH9EfjvA8h+PHT7B07CxPf/jDhOGIN996jRdefI5Wv0Nq97BUGd8+HL2qLAsLjZJQkCpP4lIhJZgkwcZHARkGjcZIA9KgTL4vEBKkbeWcb5XDzKTOm5wCMebhm7vo2n/TyLIMmT8xxpC8mMuX32FtbZW1tRWieMREvcHU5CStnV32Wy1u377NaDTCsiwefehR6pUSDzz46E/0+z7QJN6sltFCsb1+B6/kMzXdQEqFlBpLGLLOCK0FxUoNbQy2bSNEhudILBswCWka0fNiiDRx4lOZe5hYQmf3GiLs5W/fEazR28vrlEs11q+9xZS3zv2Phdz/ZIfFE4ry7YTlUZN2N2Ypq2A7NoViRnNyCoNG1BweOnmJY8drXLhY5vGPDDFZgk4tnv3+Ar/x33cZDHskwxgpD78oozhgZ2ePguvTnD6O273NAw8ucWNniY6ZZHX5D1i68FHqboWv/93/kkqpSMeCUqxpC0Prn/826cIczZPzCPIxuzRNSeIkp7RJhZAGc0SysFyHg16XKI557kfP87GnnmZufj53WJeSE8dPYCvF2p1VnvjwY5RKJZaWjvHtb3+HYNTn4n0XKPg+Bd+nWPDoD7psbW5RKpUYDAYYkyf/RqNx6DoOdraxPIe+0RDEeJZNlsZYTkq92KRSq1Cs1FlbWUdkNr0w4vMff4RKAXSYMDu/hFOewAhJe2+Hf/Z//g7Vosvx2SZzM1Ucx6GrI+QRjW+A0WiEEIKPnF+CNCIZ9VESYsejXHCwLY+yhLCzSRr2GBz0SZIIKiWmphqoLGO4N2Ch3GCz30EhENpgWYpRPyHNFJZ7ePlgrjJJKgLmp33Onq6TpjFrK6voqMfAdQhiQafXZ3d/l+4wZRjDMOlzar6JCHvMFib55g+ex/LK/EfPPM7K+i5npms8OKcYxJI7LYgijdGHb3qc0gmUcvCmJCXfwUkniZKE3rDP2xtX+Pa3/5TW9h6f/PynqVbK6Djhsz/1WR6770N86zvfY3dvEze1+L1//ltcfu4lfvUf/Kf8zKc+x+c+/gX2e31+8+v/O5ZlI6IjyqC2jRJ5rvAdC6kkalwT12khn2oTjN3sDdrk5ZW8aiSwbInrChSgxwc+hSAJI6IkwfHKKKHQf6VyiskrAOIvkv/29jY7O9tsb28TBAG9bg/Pd/B9B6kCBoNttjeWsZTLhfOXWFhY5GwxrzyEYUgvDHn2W99nsxvz7//iiSNX8ME2NtMEv+gyPTXB+voqp0+dplAo5jOeyqY5WaZaqeV1Kpk3HAw59tO1nDE61sK2FI40pDrD2C6FxgI7rVWi4RZm7DRzWNy8sczQzNBpHfBP/zvDpUdtXHcLYbnMLBTZ3a2zfCciGGa5S41l5Ud9pfBcm9n5Bk88NUOtmRDFV0mzBGSHDz1p+PKX5vnjVzyac8dIssOf6G+99Q7n7jvHZK2ObxlqC8fY2O+RDK5Ta3yEteUd3INvM73kUC/7pEJSEgbftxkWMuKOw3DvgHIUYlk2tm2PR58MSuaGvMP+CCUPr0VLY+F7NpsbO1gKWp0Dbty+xcryMp7nc2rpOOEooNM6wHNcHrn0SL6rtGymJqZYOrZEoeAzGAxJkhi/UGB7ewvXdWk0GszPz1GpVPA879B1WLZFwfNJTYYWGUJnVMolbFswMTXNubOnSXRKFiZMNad449U3qRZ8HKXRScR4P0Uw7HHt+g1+9NJrVGo1Tp/5IvWKwVI2ZV+QHMGLfm84noXSBpEppDBIaVEqePS6XbqtLmtZj7nJOlGcEEcRfsFFiHyUbmtrl6Lt4UhFrHOUrVKKwTBDo5Hy8OvU8QWNeoFzF+e479Qi15dX2Q7u4E+WmaiXObh1B0mSn2ZdiekMqFY8Pv74A7TW1rhz+w4fPnuC1+/s0x8l1F1NqFOW97uUCkUWpirsbIegD++ZrGzfzE2rbQdhWTn2N9NkWYZC8v0ffI+p2iy28nPMr0kQGuamZvj5n/sSX/vd3+KVV17EEYYbN66yvHyDS5cewVI2k40iq5s3CNMUosP56p4Ea5x4lciQmHEfTeLYdn6iGE+r3C1jwBhFC3cnRMa9nCxJ2Vi+w/5umyhJePjpx+EIfPS7kWOJ8x9uUsMwHjAcDum0+9y6fQ3bUlhCo9MQJTLurFzPjSiyDDJJrT7NwsISj334cYIw4KWXXmZvb49ms8n5+y5w3y+cZXZu7idaywebxLMUO8s96JJOwHAwQAmLYsHHtmya09M4to0R+dNVCk1mFBiFLQ1Fp4BrDbCsfJLCkDsDCXsKrzBPkF5HZwbHPjxZ7Lc2cRt9HjiTUS4OuPZ2hFAOQtpYss6Nax0G/YQ4STFZ/iQXSqOkwHNtwnCLUnkNz4kZDFq43oA4iBl2JOWZCq2tTYrVMkIdXsZYXJjn1u0Vtr1dPvPIabpbI0apoNKcpr32BnOnLrHz7Cssv/IK5WKVKI6QOqGyWKGjRrTTBDfI64BZmhKGIUEwIhl7X/q+h6UsGvXDyxi2ZeM6DnEcYSmb1fV1wiCgVq9Tr9bwbIery1eZmpxiv9Vmb3ePOIqoVmuEYcBwOGRpaYlCYcjVa9doNBqsrq5y5swZhsPhXfa06x5xXLbzsbFGqYznN/Adl0atBiJvvh5bXGBje4szp05Tr1a4evkN5mbnGPT2SeIQYwzBaMjOxhrf+/4PCWINo5Q3377O3/m5j+PZLkrZBEfUXt9bx4y1oGw7SKFRRmMpQZwmDOOE7a0tooFiYWKCNM1y1xadYGxFFMRcub7B1LEFqk6BfpKSaEk20ggliMMEPTp8J/7QYxeYnHdJgh0CY7i2vsXzb13nzn7IFz71FFpCvVGj4RTpDxMKxQG+22Gwv8nx2SYVpRj1e3R3d1lZ3UGOIhJts9/XZFnEg+cKmMgmGBzBvS8KkiTOzTeysVOOMkgFRmuuLe/hn6tjMo3KDGSCxCSkUYBbLLF44jivvPoqBc9hp7XD88+/xLHjp2k0JsjilCSKGURdXHX4icBVAqnBsRTSMvl7YlkIAzozKCuve2dJnh2UMGByUaHBvCeZ5/8Mul2uXb5CMIpQrsugP6RYqxzJE3/32gA4aLXod9vstXsYqeh1DxiO+gz6PXzXo9Vqsbu7y8TUBCMdoGxNGKXj6RyXm7duYYxhaWmJ06dPs7Gxwcc++vRdXv1PEh9oEjfk038600w06nQO9hn0hhxfOobfrI8XLt/jBg4i0yhhsKWg7LmUCgWyNMMYjTaQJSkISbkySVpxsJSN7x3uKbm4dJzZ6Zjp0jrf+t1tCrWE5XdskpHNsfvX0GlCtZgffYWo4NoCoUKkyk3vLAW3rmbc/LFFvzfigY9ldF9wWHvRwn10l+HgJEZqpDp8ZxFHKZ/8xEcZdPsEo4TCUpPkxgq+U6c+e5FXfvc57ju2xOgb30UqhdTjyY2SxN7NMLGm52gmjCEZ26aVShWyTGOtbmFbNmmSkqaHJwsw9HodKqXiXeGPpSSe4xAEuXHx5uYG586dQwhBt9ul0+lw7Ngxbty4zurqKg8++CCtVot6vc729jbtdpvl5WVKpRKNRuPIBA6wsLhIpVii6PlEJiYcjljf3CVOIyxpuHh2Cd/xSInY3d7kIx95jEptEqVsDg52GAUBQTygtb3B9ZsrTE5MEYUjhgdtgjClVCwRRH2Ee/hD/r3R7o4oT5WxlIIkpuD5dMOYDIVQGtfxGfaGpGmKZVkUSwWU58AwYac9othIKNYdXCwGowitM9wqCCWJwsPfF+VqdnY3mJ+ucxDbrOz06IaG/ZvrdIPv8LnHLhJlCR6wt7ON0Zp6yWN/r0UJzfHjc6yvaBanpuj3AtqdEJMOma0XOHVyEYWg4LlIfXgSr5ZcRsMQozWe6/6FU45SCAEHlgCRNxGzLCVOU0KdYCuN0knufJWk+I6P0ZJ33rnBQbtLrVYjiUMsKfE9C+uIk4klcy/T3Z0NeoP2eCrKw7EdglHK5GSTQqGIa9kIIzA61z4YA8rKN4ZjfwgAdJJQcD3q5TpBkuC5br5h+wmS57ulkt2N6yg5YK8Fg9BQrRXY2l4jSzUbg5But0u1VqXg14lDcB2NsBJuLt/moNPnmWee4fTp01QqFRzH4YUXXsB28kZ4PoZ49Mngg3W7TzTaBa2h4LkkUZ+9nS08x2Z+dmbsmpI3IISUZEZg2wKdJWQiw7IFrmszCka4rkt/GOS+jgIct4hfnERJhdaH77RGwwSR3WTnnU22OynNCxk3vw92L6PbDrn0yRjbMliWxa2VEmmvxsVHN/GLuaGrZRu2Ni22nnVpRRJvUsOLislVm1sM+fXf/FVWNyzefufmoeu4/PYVeqMBH3nsMWZOfYhW/4BX39lFlV3euPZN9re32Zu1qFSriCxjZdRFKYv+MGWzFZOlNpGtCPtDmhOTiIM2juMQhkHeGzBm3LA54uawLeLIoCzJ1EQjbyxmmmazSZak+OPGqGVZlMtl5ubmsCyLZrPO1avv5DZpcUy3272brG3bZmtri/n5eWZnZ/Na/REu87utFlEQsdIfkAlN76BLFIV4BZvZiRoCOHP+Pi6//irHlxY5cWKJQr2JWyigHMXu3gHdXp9+r4vtemQGkkGHqeo50jgm1SlSCbwjGmjvhhCCze1tqp6gZHskaYiWGVGgwSiUBZ7vE0YxvcGQaqWIUoo4CpHCUC15bHdbnJ6cZqk5Rae9TKpTYmMTaYOUh5cxXn/1NZyqYGZmnkGkiDKbcrXJQW/IrbVtnrMVSqc0qnWiUYg0GeVSg2MTcwiVkuqY2uQExc2AN66vEiO5eHKSiapicsKj3QLb8TmqRTDfrJGUM7Isb9IlWUaq8w8QCDtjt71Jv3+AX2iy29nDWB4FxyJJOmThCFunlBxBmGiq1ToF1yWLQrJ0hJQpFuZI8ZFUgMnY2dvmrddfxLZtoiiiUqkwDGIsy2ZpaYmPfPgjFN3S3bq3Nrn1mrLUWJYPEoHneJQ8j0wLHMvGd10y8RdJ/v1ib2+PbrcDCGoNmxs3btLteUjV5KATs9/aJ04yojDFsmweffTDvPjij0EbyuU6m1urWMrCtvOplKmpqbvN9EcfeZTRqIvrFo+Su9yNDzSJj4YxpZJFmoAtRjTLJZqlCmGScOPGdeYnphCAJQVaKnb3u/zW734N11bcunWLJE2p1aq8dfltNrcHFKqTuG6RuYk6mfAQtZ9CG4k+olHzd3/5P6FW+m3Mns3yDzM+/UsV1mZ83vjtLpc+W+Njf9vLx9BkfiRTootQxzEixWjItEELwcWKwipkLH005K2zGa0rioefsjl2TPAzX/gFovjwnVZzcoLjx+YY9Pr86b/6Nhcv3E95YonP/OyXKCWbaL3Hl376CXbPNfnRb/wLdnZDNj2HwuoIIQUNkVGtF2it3eTVP+uz9MijJFauvlTSRmcJWh/dI4AMv+BSrZYIwxG2XSGOI65fv84DF++nXC7zxS9+iUuXLrG7u0u322V+fp6pqUm++tWvsru7i1KKYrFIr9djcXGRJ598khs3brCyskK/38f3/SNr4jubW3T9A5qNBq42zE01iZMkt4CTgtMPPk59dpETZy8y6na5fuM2X/vWb/HMF36Gnb0hL/zwJfrdNo89cpFmcwIp4TMfvcTD9x3nzbcuU5+s5w3LIy77d2+oLMtod/rcWNFMNyo0yiWkA9WyonUwoNsfMj1RI4gztlsdiqUilpIUHIXvuJxcrNG4eIHX33mVRBgqxxsM04TBaEih5h9Ziz4/dz9xw+LNm+tcub7O5bev4fgFjs9NkoQRb6/3GIYRcbSB0flEROnGGufnm5xqlpipjZisO7TCmO9e36Dp+2y29pmueJxZ7VIrVDhz7BGcwuFKyaojMZbCaEFg20RJMp4AMcRJytL5Sd56/g2+8fXf45mf+zlev/IyTrFCGqQ8//0/Y299lWLBYVsHXPrUJe4/e55h3KccOKzcvorUaV4rPgoTYguksLn/kYf4G5/5WJ5sdf5tRoFybAqlItNT8yzNnODhBx/imc9+jrmlRZqTE1jS4i5tZazojMOYJE2IMcRJgnCtser+/dcyOTlJpVJh0OuzuaPx/Eu88MrzPPDAAg8+cJETJ87w4o9fY2VjnRs3byF/+AJSJbhWgdNnLzIcRaysvsIzX/ybTE9Ps7u7w/b2Bo1GjampBW7fvMmxxdMkScbExOThrwkfcBLX2pClGsu1KfgFtM7VXIvTMzSnpkmTBM+2MeRy2MtvX2G33aXV2uW5559nf3+f4wuz9Ls9NrZ6nLrgUijAqOBgF20iK1eOpUfUtD76yU/xziu/R1IZ8vQveUg94r4vljj709Nkdo+N1VyZ+S6bA6FBBGhtkEhMJsh0yvQTHoKA3c0OEw+7VC8qDtp9oqALgFKHr2Pjzh1MFjM7Nct//Q++ilOtcu3N73Hn5a8xOb0AAmwbzv7sF6hevMiLX/0VamEPRhZSGmzXYRAaRmnEVu9NEmO4/6mPk4mETGuS8e43yw5vbPqWy2AYk8RZrvpMDY8+/Aiu47C/36benOCP/ugbFIslzpw5zerqKidPHscvFnjjjTc4dfIUZ06fZn1tg26vwwsvvMAjjzyC67rU63UODg7uyvoPi4XZaZSU+L5HOOhhK5fhIMCSkmOnz6LsvLHdbu3y9d//Q3743Is89lOPMLt4iu/86fdptXOz3jjVdPbbCBExt/AYL77+Ju390Xh6QP5Ex+V3WRaZEARRQncYYluCwnhCQqncnxIEcZoiLRvX97EtC5OlDIIh1aLDXL1OdN95Xr3+FtqkKKGxpYMSBiMOb7DOTcyylbW5vbXL3vY2jVqdVBvSNIeEFUYxQqcMtYWRHrEWCJVx/4WznGmUCVp7WEpRm5hiorlF0Stw0GtTzgSpNgThAMcD74jppfx2ytPfYDgkjKPcQ3RcPrE8i6mZBgftfW5dv8mrL7/E6YunWVve4PadW1R9F6viEpRgIA5Y219hereBqyxu3bqN5xYIg+xIjaQRggyDsByKk2WC/ggSnfetkKRALwwYbK1yEIzwyx6Lz1d47WXJsePHOHPuHI7nMhoMue/MeYLBkGEcInRAnKPzkjgAACAASURBVGZkJkEZmZcCjghLSLY3t3ALReIUSsUSg2GPOB7xwmtXuNOO2BceB5kgiTKefPIxdKqJopC5+QUezjTr6+vU63UmJhsMBhvE4SqVSpXJ5gyOVSRN945cB3zASbzeqOP6HkpJytXcWT1JUmzPplIp5jPhwoCwMMaQDrssNiucPz6HKzPeunyZcqmC7xawi1UmJiqkqSLSCQpF0B/R7/dIksNvjunJKresIpu7Ixw/wQ00jrOG7bvsr9j8+XeKDBKbJE6xBEhbkWUKrSXCKIp2h5Mn+lx83MErBkRRQhQGhFFGHEKS5Dvwo4ySq5UKKsmYnp7m5o9fIFGC2NjMX3iK/s4KSjo4/w97bx7j2XXd+X3uvW//rVW/2rq6qpdic2uS4ipRpCxZ8jL22JJmvA0sJEgwhgcBMhgg/2SADLL6L8fJDOAMkADxTDIzMeJEM3I0giVblmNJlqmFIkWKTTZ7Y3dXV9e+/Pa333vzx/tVNWWLVQpiMGOgD9BAobv6V6fue++8c+855/OdfwyNjxvCYx9sc+nNLnsjw7gAmxc4ykPJqh1z99pV7sycYmblLOVE2Nd13ROHFxzlogtd9SPPzHL29DJlUdKZ7tA96PHVr32N7Z0drly5ytzcHFtbWzz51BMcHHT5/L/9As898yxvfP8N1lbv8PFP/DgHBwccHBywu7uLUorHHnuM/f39Eyc2lS2JghpW51it6XcPKIucC4+ssHR6lqgecPn732H95nVuXn+LNO4z3+kwGo65fWuVcZzy1uWrlMVZapHDU09cpFZvcnN1G89rUGpD6Ec43vG3/aTFd1KbURghKK0lTgv6aOanQqJQkWfV8YAtC/wwqKYABWRpTJImdGba7Kxv8MBDZzgYHnD19hr1eg0ReJQ2oTDHs2SmmlMkoxSvEKAt0kIaJ9TrdbK8QGcxke8y1eqQa8swKWjJlJnIZabho4IZgukW37z5FvV6ROh6xAkoVxKENeqBJQghECeHgaptVxIGPlII8tJQlJo8K0hNhhs5FGXOXnefYTbi5tY1Yp0xfWaKwFM4NRfXMyTFgO4o4mDQ5yDcp7u/V5EnBcgTuqiYNP9Za3nq8af49p9/C7TAkQrPV7j1gFTnlNZiSs1wNKQx1eLypVfYWL3GO5e/j6sc/CBk/+4aSinSfMyo38XzQ25efovlB84TNadPPFPRxrCzu8Nzzz/D9Xcsc0uLbG/v8K1XXmMnsxRhndE4ppQujXqD2c4saZpSliXtdpt6vc729ja3bt3ioLvD9tabDAeb4NSIwjpSWq7ffJWPvLB04rV5fzNxa2i3W1hrcT2/ohj6hnq9PlGttxWbw1owhhef/yBPPfkEXujzzFOPc/PObeK4ZHe/xygv2djcZ+9giBAWYzX9nR0O9ncpyvRYP1pNj0b9NEXm0DuwhDWL5xb4qWV+WSDDi+TFDLXkLhbI6zOIWot6rQbW4+zcKzz93Cu4Ucagn5PnliyFPKuOcox1jm624+4FJwiZOXcWoSSPvPgR1rcHpG/8EfH6dRbOLOKiUTtXyY0l7a3h1GJ6ew5xqSmFRRgBBjxpMAakKXnney/TnOlgJlAwo0HI4wusFotQEgGcWT7Dix9+gddfe53Lly9Tq9V49plnqdcaXHrjEkEYMB4Pub26ys3bqywsLDA7N8s7128Q+gG7e3tcvHiR/f19er0eruvy8Y9/nFu3bpGf0NrXbtUYjUZEUYjXrBGGIVEU8NBDS2ALXvnuS/Q27rA42+HsYoc7q7dYOXuGN7//GjtbGxXlzmqSJKXR8rh48QKD/pjF5QtoLVheStjbPqDQx/vhSIvrVolE6Dp4jkBJg+tKHD+gVgsRVrOdFbx5a4e5dsSUJykw5BZ0MsIKS+q4DDLD7Njy7ANPsLnXJTExpXHIZUYujq8R+EHInJxmNmoSxzEIB89x6R7sk+UZK2fPMI7HGGNw8pxWy2N5doHbt9aoS8PK2dOk1rK+tU1ZGFCWmVbABx5/mKceeojB1k2Uo38ASfvDTKMprUUg8T2L6zhoTQVAM5Y4HyN8yf6gSy8eErTr7KYHSFfgzwVYDLmoWvLKomScZPhhRL1ew1FgybGUcMJcBeKen5/8qb/FK3/2KoUpCGp1who88MgDXLl2nW5/xFD3uHHrHTbHXR564hF21u+yt7OJyQryUnNn4y7tdptms8ne7gGNep1uf5+9rXWe/8RP40W1Y10xWGYW5rl08zZvrm6wtnPAeP+A7MpN5p99jl6cMhoXWCPRZUG3ewAIyrJETxKssixxXZdub5+1u6v0uncYpp/n0YceoTszz6uvf5WPvPCLx68J7zs7ZTAZ4BFHAznj8RipFGEUYkxFH6v6Z6sJv/ZUG20sYa3G3MIC4yRjY2uXl197C11qQs+lKDK0LhnurtPd3sSY4x/SeuTRnlnB2oCyyEhjgQ0k1kCe5zzz/BS3b59jOgsoNMR+A1VvEIQ+RisevxgxM6PY3inJMkhTKHNBUUryzGBpkBXgntAyNRwcsLcVIo0kGXapNWc5+/BFTj1yET0eYLKEVMegJXk2Ytj38GNLTVsyT2BcF18qYlGd0RshiAc9Vq9fRwoPrUuk8A6nHd7T9vb3aber6vhTT3yAJx5/nLtrd1lbW2Nvb4/5+XmiWo3t3R3i78XMzs5w4+Y7vP76G7z4wotMtauz5jROWFpaYhyP2N7eptVqsba2xu7uLrOzs/T7/WP9WJifgfk2QsJoEHN2+RRh4OI74CiPyy9/gwvnzqKwPHJhhWtXr9HqdPjKH3+ZVsOhPdWmXW/SbviEjSajJGW/OySq1Sl1yXNPPcpr33uD27fXj/XD8xzU5KXmeS5h6FKr+RXnJghwlKFZj+gnBbe2NrG6JGz5jEaaNANfCrQbcPnWOqVs0Ko1WTm/xEef/jB/8u2vIh2QSmKPf7fi+hZPjJmf84mHAwoVUQsDWjWfxYeWEMJld2+XTJc8unKejz7zCE2RcXdjh0JIwmadK2/fZnOvR32mjSM1CzOzPPf8s8w2IkIZY1HIE8BkpdUVM8hoBBorLEaUWJURBA6+sZShh84knZlZzsvzvLHWxRgwRlfFRFthX7HVROtBr0f08OMsLZ/ju72rFdjqxFOuewv2/Ic/Slhrkg96ZEajE8P1G3fo92KMMeTZiLWtlN/5/f+di+fPsdSZQWrDwswsTlESTU8zTDMGewfs7u8xvnODX/nUz3L58iWe/PAL1fzKe5mtpkJr7Sb/4z//PZISNvcHmG6X/vYG0SOPIbQmFIKxLsjiMXleAduSJOHrX/86Sina7XYVyH1NWRpMKVm7s07Nk6TxAd2DtZMWBHifg/iNGze4cOECjUaDoihIkoTd3V2KsqDRbGAbVd+pwXKIfBaWqjWotISeT1GWSGnZ2NimyIqKAywshS7wKDHJAC84YUJRwczsw3jeLGl8F6OrkrXRMB4aHn3kMmfObeE5DtZWI19KWoQwaG1pNnbIMkVRaIrcwWqL1oIihyQucb05ktSiworT8F525vQZzq08gKMEtoT+3m30aJ8i7iKLGNVuI6Jlult3yXLFh5//WW780b+gtIZUg6bAiQSjLMf1DQhB4DmsXbuGt7yCNQHaltXB+jGWlTn1Rr063hGCr331q9y9e5fl5WVWV1cB2NndpdFq0ut2kY5kde1OhRYuS3r9/uSlLGg2GrTbLW7fus3S0hLb29t897vf5eLFi0ddLu9lxpQ0W3VG42E1LORZzp05TVkIPNflgeUlfM8hLwo81+FjH3mBve11WpHD4x/7IFY5uNLDlgnjfMjrr7/NQW9EELWYm5/FcRwW5udOLGz6gcuh2IDyBMpVOL5H0Khom4gE1/EJQodGzUNh2euVbO7eIstjnn7qApdXN/n+rR1qUUKjXkcJycMPPYh97if5w9f+HFvkCH181ErFNsF8wcWlc7y4+hSf/+OXcN1ZGrWIwSgmjnNa7Q7j0ZDTiw0W5j2KnTGPPvIguS7RuuDl1y8zLg2naiE6G3Lq1ALLZ5Z56atfYdQdcXZuhQsLx2ede/sZZVH1dPiuRDmHXR+iYshICAKPh555iueeexb3muCN26/cyx2sACur3aKj0KZkc2ebYZywfO4BytcMwgrMCd1L7x7EXnrwAR558jG+/dLXSYsSUhgN0mpE03FAlqA0AzPirY0Ndsc5i1MzpP0R0402ZayRysNxHFpzoLsp37n8GivnLtJqTR3rBxiEkvTHQ7a3dtDG0NvdR4zHDIdj8uGYrN9jvL3LYGeD3QCMefroaPPSpUusrq4ShiFnz55hejZiqqlQGOLCUKQFSTykyI8/UTi09zWIv/TSS1y5coWlpSU+9em/WYka9Hr4QUC/38czgvm5Cm5kbcXaFbaKgqHjISzUaz6OC73uEF0aTBFjdI60Bf3BgDhJOf/goyf6snLhBZ585u/x0jf+O3b3YxxHEoYtdnctGxs9lNpFiBHSYfJCqcBaWvsE3sPkmWDQXyfJl8kzlzS5SbOuwYa40UV294aUbZ/ZqeOW2DAYdRn2Brz5PY8bd3dwa3Ns3riG5yQ02nOEtTpjldNu1nn1i68ypRyysiQvNb52KaTCClnxGlTFiPZ1gisEpjSkyQhHHd99MDs9zfLCKSLfp9fr8sjDj7J0epnTp09TFAWf+9znWD57jna7zZe+9EXyrOTGtZt8+pOf4iMvfoQ7d+7wd//urzE1NcVoNOTq1SvkecHMzAyf+tSn+Z1/9jvs7O2ysrJyrB9ZmnNt6xa7uwf4nsPuTo+pVptm3eJ53oSJo9E6JdeCesMnG2xz8aFllClQXsjd9QNefvUVHrjwGN39mNZUh4//xMepN9usra2hdTWOfZwpX1VoVGtIRYrJc7qbB6xubjAzNc2LF5ao+SFhmLI008a1gn6R06m3mT07x5df+T7Xt/bIlE9WDnnr7jusrq9z6+42M/On+JVP/Spf+uoX2esdHOuHFh61U6dQQjIz/ec8/uQ5Gs02nivYO+hT9kc06iXPP/0oP/2Tz+Dbkg3boD1dZ+Ody+RBg5/75EfpfuEraFvw85/6JKu33+E/+6/+GzzX4dzKad65/lVuHLzN3/ul9962v3V1ZzKarxCH7XpK4rourqdY6Pi0Q59W02fY36IYH9CuSaSQZEWBMWCNpCwkaTlGM2Z16zovfe87fOLpD5GmGseRyBNBXHWwFXNx4+630PE2CzWLKAzK89FpNcUpJ6210sB5W9JQgrbQzFIQmhR/tI+y6miyMzQpZbrD3/y1/5CnnvkJjFHHQuO+9o2X+cZ3/owbN++wc+0aWZITxz3KPENYwVf+2W9jjcbqap3+4PKrXL99nV/4hV+gKEvmZiti4WDQx2LY3RhBqkmTMWke8uRji6zfvcbefu/4BTlclx/pu/6KbDzok8Vjdrc2+bEXP8j09DQmqpEnCXma4s8F5GVRdXUYgxEKOylyWgGOo/CsN4G+p2hdYk0OaHRZkGXVm0ucqFVicL2Apz70c/yr3/0Sw6SJpFIU0rZEW0NZplibIZVAOQovqKGkV5HzVAshHISwbO8cEIYhaXyamrfOU08uYq0kSQqUI5mdeu9zvl5/wNL5B/CDKV745M8hv3UJrzZPbU4x6nUJ823KrddROiZqzrA7HoKxjK3BF4rMaA50Boe6NpPBBiEgjxP0VA0r5YliDJ5URK4HWnP57beZnZ1jZnqG3e1ttDE8+8wzIBUvfPgFpttTvPTSS7z44ov80i/8MuPxiHNnz1GvNyiKkkajSRwn9PsDPM9nbi7E9TzSPGNn9/hq+2A4ZGN9i+Ewrrbp7hTDwYhkPMT3PWq1ihSodYkxAj+IwPVAgtICUwi2t/aJxyVeoDC2IKp59Id7GAydTpNm81FOLZ461o+izKvOJCkQblVbEI7CGMH+YEDVnSYYDTMwhij06Uw7nJtvUnchnZ5hqTPHuMjpZylxmrI92CdxBIsS9r83JE8K9Alj5reuvcPe8C51J6DfjalFNYwokA40apa//ZM/x8LpBs2pFq3GPGtXb/PAo4/R373L/MIsndkZzvgOb12Z563bI15++TsM+vucXl5CKgcbO2SFZWv/+JdJa9pjNIzBajzHxWjIC0NeFGSlZisf0vRqDBoHdPe2sSZDihKlHERZ9UpL4aB1gQLyIkVFluG4y+bOdnXsJwXihC6Zt9+6VvV6I/iXv/1PkN09Xji/jEwTjIVI+QTKxXUkQlWkw+WFRdpBRBgENBoBcrKNqHrcQQqJT4N02ufczGm2Nw6Q0uXsqffmeI/yN6k1+zzxgUW+9LnPUhQGbTJ0WYCVhGGIFJKyKLHknFqcYWtrk89+9rNAVSQOoxpbW9u0W9OcO3OKwN8niWNGozGBhPX1TfJ4dOx6HNr7GsRnptv0ej1CP+LM6aUKMaoUWZww7A+I0wSkwPd9jDBIK9HKUJYarTWO42CEoLvXJU17UEHKKMuSOB6TJtVEZXlCdwpIdFEyO/cItfYTtOciXCXZ3d2k2xvwxhuXEFiErLD5WmuUdCfju4Y8T6lHLT784scYDrtkRYrvRaxutci1z6c/45Jm+YliDE5/wO7WBsYqbrz+fW6u7jI7ldLyO/hhhKyHMHWeIEgpkoR+f0yqC7DgSUvPLfACSTaspKWMtVhTBZ942KeR1kGFZNnx6yGEIKrVKvWVImFrawvf9dFa0+/3efrpp5memWV2do4nnngCpRSf+dXPMDc7S6/XJcuyo6Jlr9dFCOh0OtUE40R3M4zCE1sMHSUJQx/XVfR7+yhnhqBWw1WaOI2pNWq4rldlWUJxauk03d4AI0G6LsK6PLByBscRzM93mJquqvzvXHuHZn2f5TOnMIA6QZ7tcNjMWINFYAUV31wIcl2S5CU1D5JxWrF8fI8pP6NTc9GlZr5ZQzouBgm1kGGecGN3j83BkFubt6j1QxIKrD3+8bu7us3lP3uDM6em2EgyarWIuflpdjbvECiHH/vYM9QWTtPb3mD7xhbZKGZr4xaiyOhMT5MMe6hBQT3w+eDFJp32NKubdbrjlEZzir2buyTW4J+AIXhguUOS1BgNR0jhoaRLkVfDP8YIRuOcYTKmPxqQ5iWeW7UPWwG+9DHaUJoMz7WoooYSHtNBEyUMg2xAWSY4MsQ6xx+nfOVPv0EYhjgCbr/6Tc43GpybauOWGVpYlK2y8KqdVeEgaeuSU0oT2JKGUEjPw7ouxosQ1lZF3ULQU5Krr7zBRhZyEMf8g1//zHv68c1v/z7ra0NGA8nDK0usb+zSH2Y4novW1ZRlkZdYY2i0AvxA4KiI1dVV8jxnZWUFJSXdbpfhIMV3DWcWPephmzzTdIc549RBi+Mnzw/tfQ3ip0+fPpJkO2y3yfOcrMjxBwPSND0qejqOM/lTUQmLomA8Kjjo97l69TJ5mlYj+hayLGM0HL6r++EEJRtb9aynqaUVOfzBH3weM9n6OY6DsQl+4OG5Cq1L4jhBOdVIvxUWXRYMhzt8+1t/wng8Qhcl0lEIoWhPPU2WF2R5cWKb0ikraHY6uH6bBx46zRtr+5xdDgmdgsHONk53SLm7yv7OHmEUUHMFu7qippVYpFITDZKKC6GtBSwOCiMk4/0D6qeWq4zxGGs0m8zNzdFq1BmmFcjn4OCACxcucPbsWWZmZphfWMD1fEajERcvXiSqRYClXq8jpSQIArTWrK/fJQwjylIzPT3N1NQUp0+fJi9yxuPxsX488sgFHn74AVqtFl/5k6+QpDGj8Zizy/NEUQgIfC8iiqLquENr5EQNCsBzHRpNj4cfPoOWMNtp0e2npBn0eykIS1SvkR4fK/A8l8IYtKna+qSoinqFsRgk41wTZiXagOcHGOkhREpsJPghkGGMxViJKxXLC3PMLsxyEGds9obcvHuXJDak+fGZ+DixHIw05zszpHfWUEVKmVUZ+SjOsFpjRYTQgu07N1mc6dDduM3i/Glc4bKzP0Tbkq3tMb/06WeZXj7Fk3nJS1/7NpfeuEY+0ZmMByfs1BS4UUDgCLKkqESNXYFfj7C2JIgixv0c1xeEYUScjUjGBY4n8KyoCIXKkhV51UdfWOpRDawmTlOkkBOZxRNEIYIG1vXI4wGRTVDGI8tjDAatJIUu0DqnFBprLL50qXlNik6N2c45ZqbPIWt1jO9jgw7SFPS311B+wWC4RqeENE55/dKbx/qxub7FrWt7ZGNB0q9EUgxVTLG2ilfGWpQURLWAXm9AoxYdaSRsbGzi+V2EELTaAZ4L4+GQTmsBawyjwjBMBcWJJwqTdfmRvuuvyHzfP+qR7Ha7QBWA+8MBlqpTxVp7xKFQSuE41flkXhQMBn3evnKVy2++QZElaFOxWIqiIM2yI7LYSZqS1RSjJh1nPP3s81y7coWrN++Qpmn1gRJ0SUVWA4yWFEV1gYSQYBXGaPq9bkVNA3w3YHp2no9+7GeIk6Qq+mTH35SvDfu0Lr1FqzHFN755mr39gNGDD1KbWSRq9tkwq/SbDwN72CBg7kMfYu/OlymzMbGGoWOovYvrI6v9Q8VStpbexhb1uVMkyfGZ+FS7Eihut9pHWWpZlnS7XRzHIU3TSQfLFMYYlpaq3tVSa5LJ7udwOKYoimonZQxTU1WBaGpqim6vy3B4vKbk3GwF6qrV6nz653+eXr/HVLtN4DvUahH1Wn2CHpWUpWY0GN3ToNQFZZlUNVwB/V4X15EoIXFdhxLJ1Zt3aDWbzM3PH+uHlBUN0REC13FQE2FmJEhH0ssSiqIg04a6V0HGlFOjO7aoQKCEjxUW6YRoETCKLUo5LHdmeHilxcMXLnLp+irf/t5rx/phLORWsj4YEnoOAkNvf4/ZhVm6oxgba+jH2LjkxrVV7t7c4OLSIm9/7/ssnlthEAtevnKdKze2aNSbOJ0Gc67Lc088ypVLl6nPz3LzxirOCXJ1AonWJUIo6jVFWWjSJEcXCYiSIBD4ooHvOvi+RKqS0FeV5F9qyXWJUBKJg7UFnl9B25I0qeZEXJ/SiBORuAKB0ZY0zVCnlhnpkrRMEWVGKaoBJiUdlOcjlKXuhQzCiK7rsjS/gG2fInU9rOOgnDppOmYsHBqeh3YCls9coLc95oGV5Fg/djf6hMqn1lRc2zhAyxAtXKwokaJ6dgSCIKjQuEUmKX19xDBK05Q0LUBovLBgetZBxwcMhzuUxmd37SrlqAfOjzZ3/74HcaUUcRzz6quvVlmvMRMOg2Fufp5Go4HneRWdjKqHNElidnZ2uHHjBteuXGd/v0eGAumhjSRJEtIsqwBAU1OcPXv2WD/icU46TNnvJiyeucjFxy9yZ2ODJKuEmkutKfJqAMHoSlW9SDMQOXKisK0m0lnNVoPO9CwfePIJPvETn2Zh8QLjcUauDdI9PuVrzk7j+S77vX3+9Ze/xubmDt/8xldoTU8dapzzB47DfrfHwmyHXp7S1zHKQFdZAldVW8cK2Fv1SYtK3UgBpkxYf+MSYnbhWD9q9Yhmu4kRcP7MObY2N/nud79DUZQ8/PBD/PKv/B3KPEdJSa3Vot1qIYWgLAqyLGM8HlOWBbdv3zoau19cXCQIArIs49zyWaQVrN2+c/wNkhuUozBZQaddZ3FuGuW4ICrORJqkeK5CeS5hECGlizbZZEcHZSnp94Y0Gg1WTj/I0kLJMI65u7nDpcvvIErY295hY2PzWDe0KPFdhZQuFo1yJGIysm2E5crWHVzrsDS1RL8UlIMxqdbcXd2jMTUDRnMw2CE3lpn2As16ROT6hM42rTBi8dwDvPjoB/nx53/iWD/+1id+nhefe4FvX3mZb66/Qme6gZGWsp/SED5f+D+/wObBiFev30ZGbQ66fRryLbKyIC5eYpSmNGs1ao0pLr2+ytzaCFdqvnnlKqNEcW72An/n1/8+Z5bPHetHmRjK0iCFJIhcrLTU/DpFodElDJIxySBn/swcN9e+x+rGZRZmJK4bUSQeaZZT6JLheExqMnQOW1ugc0kjbBFGLlkB+oTuFFEWBH6ECSMWf/yXubv2FnkxwPckVljatUUwHs1ag8hX+ELQCiOcALKZs8Stc2RuiHUgdCLS0YA8y0kpCdsuUnWQSrC8/MCxfvzHv/5fMh4PGQx6/O7vfZbr1+4yFUV89KPPo03KF774NU4tnOLM0imklGyKTYbDmIVTbWZmW/QHCW9dXsXkml/8G2cxckhRhFWXjwvklrjpkiTxsX4c2vsaxKsChqRer/Haa6/j+16VSYlKFfOpJ5/CGoPnuiinKmhm2ZhxPGJjfYPVW6v0ur2KB61cylyTl5BOes1d3yWsRWxsnfCQFlDklTJMWG/zS7/677F4dpmv/9m32NraYn//4GhYR+tqyyskeL5PGAbU6w3a7Rna7WmefvpZHn1khZWVFfK8zv7BkCIZY4Rkb6t7rB83b2/he10KU6DznDTPGY41BNX5tu957Oz32dw+oNOpkfX7HDQsJleYPMeTkpyqLmDtBKEqK8HhQ5ymyWP0+PgMGCyb21sIIVi/u8HspHq+urrKpUuXePTRizz11DNYY5Ci2j4Wk6xCSsnu7g4vv/wd1tfvMjM7c0RlOzg4wBhDo1HB9memZ471wmhdDUhRZS9YgyoKlOMirKYs0qpP2Wq0LqouJqg6V5Qiz0rCsF5pcmpNoQuE0NRrAaHvQpmTpQWuc/xQiVACKQXOhM1+qIJuTInnVcVAhGCYFCAdnECS5H265GztbVCkMbWWwo0Ctkf7rPc2cZVDK6zTqbfxOtOIQUw4Oh4RLJVktjXNz3zoE8wvniYeDUjiMcJkRG6O9STaSyFU1KcCoqmI1VtrKM+j3mqj0hyTZ+Q25/N/8ud0nIDpRsTbmzs8+OCT/NIv/gdEYftEQBrWooTE8zyKrKyOFa1A2kqYIfBjgmmPvYNb1TXRDlI41XGSAxaJzSxR6KCEJB0XJOmYyGtXUDGbV+Iw3vGZuLQWa1ImZgAAIABJREFUTznE0sUUAUKEJPkIi0uepdR8n3pthlrUxGYJjueQjFMWgjqqdHBFSCkitATtBmQiRrsBVlY6rUZ4eH6I0scfcw0GW5RljudrPvLhx/jAo+eR0mOq3WQcx3ziYx+gMz3DbGcarTWeGrO7Y/nQMxdptiJ6/SGD/V1MaejMnkUjMMahEUXUo4iyqLL10ejfwcKmMSVS+Agh2N7eZmlpCSEqMd88z7l+9SrCWPzAxZkE8eFwQL/XZ23tDvu7B2RJhi5LKDW6NJS5QRnN4sIcq75LZ26W8oSbssxz4vGYXj/G8X0uXnyMmdNnWDz3LLdu3mL97hpamyMSIFbgeD6+7+P5HlFYI4rqNOoNnnz8Ec6fn6UWeVy5tsNgNKDbH2OkYv9g71g/0jRDCEueZWzcuYvwfBxhKVJd9XyHXpX9acvW5iZJZnEcSeFIKpRUheOtoBPVMMWhPiTWIpWo2jBPqHJ3D7rMz84dHZ80Gk3OnFF0Op1JIH+D06eXEUIyNzdHEATV0ROwvr7OF7/4RW7cuMbS0mkef+xxjDFsbW1N5NkUUVTD8z1WLhyf4WhVCWdrqXCiFmmWQWnxgFJY0tKQWYNjND4KISVC+FUBUYETgldKkjQlTrJKw9UY1jb2KFF49Qi3NCTpSVRHCaUBKwgCjzhPkY7EEx6eIzHGIqxDbzhEBQ7KarTIyD2NFg5Bo4EKS4RTHTkJT5Kh2SuG7O+MUfUAMolzAk1RTyRo3NLlxx77EFhbqcNrTZrGfOnbX6J3MMI3AeYgAUfS8AOa9RZZXJANMwLPxS0FG/spcSjw67P85I99ko997GcIW1PosjyRWVLtiMXk52YoqSeaqV7FkUFiKBjG+yjlIoSDKSvqYClypATlWMgnWFpPYaWD1gnrmzcxeVHNM5ww/WR1SZ5mFMbiG4/Z+gyJ4+K5IUWe4NgAnRpyCtq1OqEjCITFNRZValQ5eSaMwCKRykUoFyMtxonISo10HKQ4fq5Cl32MKdE6Z+XsNFJ2sNZQ5hpTtjl//sMwoYcaY1heegZdVryjsswIvZBf+uSLKFl1urlerSpyC0GWO0hH4gYRNdU44cpMrs9fpSjofbtv9+2+3bf31/6/KYLet/t23+7bffv/1e4H8ft23+7bfftrbPeD+H27b/ftvv01tvtB/L7dt/t23/4a2/vanfLCR/72pP1ZgpQ4UiOUg3ECQlugkROwftVqdFh0tZNJRDuZMDscLDETkpHneZxenCdJYkpd4rgO/+Z3/6f37Ff673/jv7YCw3SrSaMWgp1oBxYFw+EIWWvz0Z/6WZrtDl//069y+dWXWTn/MCvPfBSn2UHYjOVT0+x0x1x64yqCasx2Z/MdRL7NSqegMzWDFS4/95n/6D39+Ke/8z9bqyQOgmGSMt5YJx32mDp3gcD32b5+hXTUZ3Vzh53dDXSe8cwLH+fSK98mzRKEUnhS0PJ9ap1TDDNDEAYoZdjrbzMY7tFuCxwl+OM/vP6efvzWb3/GmiKj026TFjlZPsb3agjpMIxHgMUYg3Qr8eQsjplqzODZSry61AXKqVgugyyhLHJ2dncpE41PwOrqJgejBM+T/Jvfv/qefvzmb/5T6zguYRhOtEFh4dQcZZmxvbNJWpYIIfE8F8dxiaIIXUrKsiCOk0kLq0JKQRRFeJ6H4yjqYUjgV5qf29vb9Ppd/tvf/I339KNWq9l3339/8WtHUHX/TCZFjTEIRyCFOII4Cah69icyb0ef867PUlJyMIzfez3+9cetkg6u6zAsMzzXw1iDMFXLn5BVj6UUcjK3AC7mSIzEUQrhOCAVoY1wlUfgVl1Wvh/gGih0SWENP/nkbx3X32ffjYH9f5/7iaPfXQAUCf/yt/4xjXaTX/z7/+AHf8wxQNrPfeFL1phKvEQphbVmspbiaDhaCHkUHw6FPcQEdHVvotKys7mJFYrZ2XlMmVWQLg6FQCT//md+8T396ISRlZO26EOVKCNAGXCtwDjVQFoFCPMJwpCZmVncWogbeChVDTIixUTIuXq+7MS/bBhTlAUW+NNvfP3Esc33NYgfNStJixBVa48SglAUOKLEWH/SNib+khqNtYcXyh79EcJOLqSZkNWqNp3DKcL3sizLkBjyPCMWkOQ5SiqyLKOwlnw0QKIZ9vYZ9w/oTLfY795lprfBw+eW6e7FxOOMXCgyk1JmKa4AM9yiwYAzc2cJoybaniDYvLVDnCaEtQbLpxbZqgXsbg/Q+9vU6k1qjRrj0QHDUQ+hDVFo2d9+G21KUg22LMltSeA5zASKZrNBdzRi1B8x7bcY9UfE4wzD8RObC60OuszZGeyDkmhjcCS4SuI6qgJACRDG4koH7fnYEjzp0oxarHa3cKzGFCUdJwQV4EzDuBvjWo+9IGSclRhz/DBHNaUrMaaaDtRakyQx4/EQYwy1qIY2Bs/zcV0Hx3EJg3Ay4XuPU1896JYgCCYBPQRryPN8AmM61o13BQHxHlJuP3hvCjEJ4JNgIuxh2BJgLIqKu2KoPtOaKvjYE3CK5vD3KcsKlTxhsVoq7IJCHhE/q2llgRYu0lQIZCt8pFQoa7HlZHjNVMGsKAqshtJoThiUBAxGlxOpQoE4gU//F9dK24ocKYVi6/pNXvq3n+PLv/u/sfzQAzz14nOsPPkck1iMOEY82phKTOHdYiuHa3LvWk3G3ifrcfiiPbymh3HF82rs7HWpN3NcOfk3xNF1P/m3qkxYUAi0NpPrXM14SGMRpQFRgJTkeYJxJi8zx2CVwsqJz9ZOWpmr65jl2YSi+aOt8PscxCeJuJX4zoDSehjrcGa2iVKGm5tpNUI+CeKCdy3W0brey9Invz9lqel2u2RZitYabU7oEy9LosCr3tpQqaIXBeM4RgjBIB5w68bbTLc7LM61uBt3iaVHqjPyMictCkZxgu8FzNY8drqbKKFpijFLUxEyL/AbDkl5/Nj9YLhHUhg83+PlV77GnY01RqMBD/k1Ail45+o3GMdj0sJQSAhrDkmyR1RzcLwG9WaTqXaThdk5ZhpNap5imGds7x8wGCa4B0Pi8QHmhIcuzhK29rfJdM7M9DRGu0ghcER1g5bVRUEYS+AEzDcXqFsPpVy0C2Od4QlJmmckpsBDkudVVpyOcyLXZabZxHVPwiGUkyCucV1Jnuf0+z2sNZP+Y1XlhKWpem4LjaBSSlFKYYw5ytDyvGK11Ot1gjBEWEMURaRZdiKR4oe13f7wxMIeSbgJTIU+mAR0JjRJ++5d5eS/W1HhEU6KFYdq7VYKpL23dlZMAF2yQi1Ie+9naOniixJfWkqnUtURtiDwPHzPA6GOhtikqV4SmpOjxWFg+1EC3F8ya3CEQJiCf/IP/1PeefW7+GXJ6tuX+cf/+X/B//DZLyADt9IROOZjpBTcuwSWe3rG4l4Qn6yZnLypq936ve3Rof+1eo3+rVUGVwc88ehDCCmPvu0kUNu7TViLg6wUtpxqbYvqR+FYgSwNnmsrNrjr4AiFtFVCdHhJj14wgC71RBdXHw3snWTvaxAXQmCxKOPy2MIWvXiBW32BMCkff/EDvPP515GiysSrh9H+wJv2L2ZIh19ba+n3BpX0kdEn0gOzNMNzFUkSI/ERJmdr/S69Xo9z585x/uxpivE+/WSfdhgRt0JMKmkoS7G3ysHaGjtphpfG1FVJ5KYoZbHnZ3EdSb3ZxpCjOT6In4oiCmPxQo83dvY5PX+Ot/vXeOfGTepRjZ27A6ampliYa9AbxhiTkxSGM2fP0ai3WN/aZJwmrK2vMe7M4Sm32tY5PvWG5OHz59ndiUhOoDruZzFuLYRMVJwRq1HSIfKadGqLTNca+K6Hch08x8HkBQfbWyhHketKjd5REsepsgsjBKMiI4kTXO2wvd9nVBS43vE7kyQdoU0F6h+OupQTrobvh8zMzOFHNZI0RSmF51UZW5IkR9mZ63qAJUlSsizFGFMhGcZDZmc61YCKcnCdkzQ2790/787Mjv5+8rI45MwopdBljhISJQRKVKLCUoqJb1TqNpPAY6kC+onAZKkxVlKROEQ1DCNBCmcS7MS7XtAe1hU8NrzL+WbOIN5lnM2z6Z4hdx3Wrv85o9GIlQc/wMz0Q6SmC2ENhFNxPE6wd82UneB4NUgjKAGJthKJ5Cv/6//Cb/+jf4iLQjo+gzTBl4add97hX/2j/4RHn3+BM089w+LFp471QkxAQYc7cOBIALtaV4ux1dmIBay270oE7y26F1iefu5Rtne69IdDpprNwyk5OCF4Ht4HdnKEUvehDoyMoqszHpw7xV5/h6YqaaOQUrKXVdhsFQhOnW9wd7t/lBQYY46ycWMMRV5UBM1/NzPxCmJjleX1775F+5TB9c8xSlK6u+vv/qYjJvgPywB+6FZ38uAIIU98OrQuybMMR1ja9QBHCh5aOYvvP4Tne3iNNq60RAqs0TQaNRqBIoj3iO8c4I9HiLzAReC4DlppEJa01HhhnczYiqB1gh8qaLG4vMid1VWefOLD2DLjzOnzuJ5L92APX0Jpqot7ZuEUZ5aWGI6GJFlMlo1ZWTqDNpbClMTpiDgfU0mUGkpdUCQpNs+Y7kwff02KElxBURQ4tuKLt6Ip2sEUjaBGI4hwpCRNUvI0xXVchFRkcYxT96pRfCFQAqwCiaxYAI7Gx0d5CsoccQL131pDUeRkWUpR5FVm7ThIpciyHOUFZFl1JIIVFGVBWRZHxxlFkSNldcQQBMGRjuFoPEZQTTv6vk9n5vjx/6OH9C88RUf3oqxwo4dB3FpbgbmErDLjiVq6OPxaHCrhTAK5tZN7/HizWYJSAdLK6r42FYxKTcBeUskqiJe6Ek+xBd6wSwZcv7GFaTr45xZxEOz2EkabOdd7r5M+eBstJK3OCs3WMkafFC0OA5v4kY7DD3fQxlqUUOyuvsMrf/xHTPsOTlgnAwaDkilZY8Gvc/u17/O9b32XqQce4Td+7/84xo3KD8EP7myEsEhRQe2krHANR2muEAh7yKWY7GIQVaInDHc3tpFlwcyTj2PsIfbhRwNPYS2ZVUyHlmfOTPPaakKSOpx/6Aw73zogUJYXn+xwZ33M/shSWEXkB8y126yv7VJUqKOjmslh0qqL4t7L/0ew9z+IC4FVBm2aGD3GVSVCBXQ6LVxnD42o2M1Hw77cO0s5fIjs4WZq8mDZe9vWE/eoQDMERxW0ajVqkUeWldTDkHqthnEkXuiiEER+hcSTboGrJWU2xpElwXRAbnyKQiCFpdQKhEXrDBUKSlsF+JOODmdWLrJxMOIgi2i0p3n0dAM3DOn2BpxdPEU8HmMsxFmKkorp6Vmk5zPe2iUuU7JuD13kSMehPtXCM5ZycgO0HA856xHPjUjj40E6pizBqcBkPi61cJr51jyBdPHdAAWUaT5hp1TBK2zUkWmOkQY1wbVKKbFKIScj8VJWOpWO6xzVOo6zLE/Q2pAmGY1WG8dVCMdFKMkwHuNEIaUxmLICppVFRlHkOFLhqCrjyY1Bl5ogjBgOB0gl8RyXLK1eDK4f4IfHKx3BX0wc3nVOKgSgENKl0AZrNFJI3ElQrgiIk2+1Vc3n8LPsIfP9cEd5QhgfjQZEAUSexLqSwrooq7AGShdcC9JUDHmlC2TRx28aXruxy9ZBSDHeZzlaBRvy1mt91m+VPPGBiP1gC20dBrdyzj5WQ810fqT1ALDGgBTH+F7tNUoLUhiGvR3+6Hf/Bbff/D71wGNhdpqDNKUbeph6DVpN+q7DzfVdfHvzBD9+8MX67usDogJouW6VKB7+daUUfs8zU+2HJILSGopCM1WrHRVBsfyll/cPcYJDemnkCR4/N81PfegcV1ZfwbfglAecilyeX6nziY+c4Z9/7g1yA9KkCO3hCIspCrQRYM1RsftwR6mL8ujrH8Xe1yB+qDpjgULVMMmQWjulKEIWlhdp1G7THYvJy/5QpabS37OTggH26BG5d045uQC2ekWf+HCcW5pndnYOBOTaIjyfMi8YJznWVyg3q85zMzMptlmsclAEGGEolUAELm5uQGtcP0CXJY60qDzFc31C5XICLprccelMR8x1VphqtSiyMa9fv8va5ojTYc5Mc4rUFkw506RZzhuX32Rnr0+pHQqtOXtmkbnpSrzhIC7QeUk9dGnPNFg8dZa9/T6nAoerb75+rB9xZlBYsjSj5bRotufwNIy7e+Suiz+7QFbmGK1xlEOv28OtOeBUxxOnO6dI8jFpliGtRokK3+pIifQclJQ4SuA4x6dx49EQiwQrUNLFWEjTHCFSrLXE6bDipFiFsBV+FlsVt4u84s1YaxEWTFlUZ+xOJeZRlgVFWZAVBa570m1/7/65F7vvBeOqliLQxmCNwXFVlYFLWWXfUuIoBUdFuMPz2kOehgVRqccfZ32RkZcj8By0FFjlENiCwFqE8ShNiWsnHPlSE0pBVG+QiJL2Qp1xscvO7U3SccHSGcvZswHNsEmn0SCqF3zxs7eYPfsotc5JmpLVG8oaC5NuiurF9l7+V5mvNJqvf+H/4pUv/yEyyyqODpLYaF78qZ/gZ3/t16lNtRl0B3z1T7/G9OzxiOB3H6f+gHeT9S2LAqkchBRHR1ZKVMr0hnvB+TA0xnHKeJyyPD/7g7/qCcnGYcyRwuHByOLlGcPhDgudgLwPB+u7tJuK9pSm29vj4pMrLNgmRWE42B+QZUNKC6YoJ55Va2sm97IpS6yxE32Ak+19PhOnCrLWkpeadBwT6QyrDIWxnJ5rkd7t4QpDVmiMkExqv0wAfZNfucoEDrdG7w7kJ29SYWZuHuU4ZGmGVS5aOeS6xIsCXM/B6qqSXxXOLJ7rgvLAcckLjZYWx3VRNscqSRCG1fltqti6fZOFBx+oOmVOAPp8+f9+kwcXLfl4xN/4mZ+m0Zjno505RsOEvbU1QmGgEeE6Cj/0+aM//COe/dCLXL58ld3dfR5cOQ9FzDAp8NZvkQU+YfsU5x9aYfn0Kbw7t8i1ojN37lg/Fk89QvdgE5PtMTU1Tz30Ge3ssX39Os1GHSUVbhQhKHEdlwwYDYYIR9BqTrG4sMB+f49xOmJsE1zl40pFicQoMclQ77W+vZdZK3FdDykcnEm3kLWW3FY7iXhgqTfaYKrjA2klrlupL6VlinIcyrLAsRad5/hK40qBLTOKwuAqjzzPSE6gw737IX53ED+8v6RSYMVEUkxgdAGqepTkpG2sVqtRZFWXgfyBXYg6qvOctB6BErilxitKcs9D5Bn/D3NvFlxbdt73/dZaez4jcDBf4M5Tj+yBzakpUmyK0WDFJVulSC75RSk7KjuVsTxUJZWKX/KcVB6SlJ04ieXIlqLEFi3SFkVRpFqk2Oxmz7dv950vcDEcAOfgzHtca+VhH+DeltRA56XtrwoXKNQFsM7eZ3/rW9/3H5p5j7qRjOUMaQiekCgBEodcO9y9VVBvzHPj3gbL8w5OGHGgelTcnMkkY5IPsJ2U3bdTzl1oETRCUnNCtVGOYTG6YH93h+bsLH5Q4WGDvDw1Ww570RZXetx/+8e88s1vYuMYhURP20up1vzkl1/iha/+HOUTLXj+xZ88sfj6s/fmQ98H0kmC45btPWvKZFsNPYaTdFrtPvwdUkm6nQPu3V3n4unlY5BIfz4MZbcQa/ji05e4v7HBG7d79JKCcZqTtCH0E6KZqxhvhnPn5jjtSGr1Fv1xyq3NAbVGjfZme/o3H0KmrTWYaSVuPsbAGf4tDDblIUwparLX7+L12rSWG7z73h1+5sXH+cEPfshCs85+P+be7ojtcYgRsoTbCIkQ+bSVYni0dXUIJ7If4zhkkCRZ6dRS8SSyiIlqHo4jqXg+uROSIEqkhJAUUlIVGUZ6QIGrM/wiJ7ea3EqEKRX2djY2GfWHdG/fRfkeQRDw5DHr+OoLrSksEm7evnHUG5NCMBr0uX79Gv7U1kwIQW4zNrfXkUoTVRU7extkWUJeaMKZEOlIJuM2Nz4YsbFxiyLPGA1H7OxsHrMKeObcZZLlM6inXiQSMejS7m6cpAzjnE6cMFsPaMwv4juKsOIy3u+RDTMmSOZqTVZnlrA6Z+tgE0/41Fo+Q2/AYJBNq3JxctIKQnwvACSt2RnSZEIcx3S7HUajMWonQ03F+8dxihe4yNAQ1qo4gUDmKVVfYsYjslEXZRLyIkO5dYL6IrizqLw4Eb30KCztsPIuP0//g3JRUlD1Ap5/5kle+tIXabf3cB2HWzdu8ubrb1AUWdkrn7Z5hChfvxTiqCfrnTBgDUyVRE/Y2l/nc3sBk8EAXVikKpivudiFJ+hVK7hpTs3VqIHmxnabSq2CR5P7tyc0lkZ4keXHf5jx+LMN3nhL8caPMz79qZSf/GsvULgu2YlSxZLf+of/G7/93/8PnGpE5JOU8NQCZ55+EpUWjIcdglqdMxcvcfGxxwnqDR7cvMM3fuOfMNxYL/H7ChqVkN3eHn5zlgvPPD+9xhkWifLlie3HR+MQWgmAERhr8BxJFsc4foBSpVfBoD8g1+B4PoftWYEgj1NUltOshOzt7nLhzEo5WLQG5yNPGI9eEYHE8urdXda3E+SOnPa3IXAKfOHwx39yD+Xu8Nmv/CT/9Pf+Nb/wM5/ja199lrjw8a49IE0mRwCOIy6BhUIXRxv9x4lPvCduAYTEOgGd/oRa1GV2fsRbP36Dlz79DBdX50iGPc7OR1RDl9GtAaNcolFYIZDT3uMhbvzPJ+zDscpHh0EgHYUjBMpxUUaToZhIj0634CDtkerSXch1XYo8ZzaCarVFPaxAkmOKhKxIGGaWxUqdzKRsdPZp1ipUZltM4pje4PiK7+7GJkqU8/xKpUqhp3hgaxgNh0jPQyqHLM9RQlCp1FBKEkYBYRTi+z6O6yClRFpLrgsc5VGpVqlUIibxhDDsHxaJH31PjEHnKY7nIT0HPU4Z9QZs3H3A7t4EWavy7JNnqc0skueaqBow35rj/v37dPY7NOoNfD+gNdvinQfvUfOrpOMJSZZSYMp7JqY2csdEp9vG9yOkUMzM1IkqIbV6jeWVZfqDPnduXi+RILYgqvv0JwfcvXUTlCJ0PZarTdYaLUSWUHELdBpjSInHA7SjKVQFowSVxvE63o8inw6Hk49W4qhyBrK6dornn3uGpaUFlldWEAhmGg02NzbYbbePNoPDny2HnmULSipF6B0veer6AYlTgBeSdSyTgSCdhBhbEArQahdXtgiVg01ydh5sIk1AZzvEreyzdNplt+1hupqF+QXefKXNzbdhzpHUa5JGa44sz8nNCVDYvS2++/WvM9rbZSznMQjuvX2Nwd4uV8+s8s6PXwMBb3o+UaWCcj0GwzH5KCZyHLyKh+e6uK5DlqfMtGZpzrWwnHQC+HA82k75sAZ62T6pBj65FUzihLBSwZGKTn9MtVY/QgMdFpLd/X0OtjbY3VonHvV54rFLBGFwUuoo/9p0N1dKMcwsRrpYY6k4ltBXZFjcIOIgznDygiwriGNIc4nre7gOCK1BG6wtrf8Ojwmlrd/H74fDv4UkzvRGuI5kplGnUQ2YDPZoLLQwWczyfJMHcZ9qoIgqIbFxWd+fsDfIGGV/MVTu0YeuPJocfyzq9/p4vo/reaRa0k0Uu5OUnd6ANJV4UUBhCibjPo1mk0oUERufeGvCTKBZqihkMmF0sEViBFF9BuWFSMfDcX3G/RGT8Zg0OZ50FFSicte3hkq9Rp7laF1QFAV+EFKp1DDWUA9CoCTDVKtVGg2FkqXrt+O6ZVXnemWylLLELU8rR9ctrc2Oi8KU1b9DgZiC14LAR/keuYhZmplFVSIKVZJDXKnILNTqNfZ297h+/Trnz59nZX6l9BsUhpGOGcYDlC3dnKSQH+ONmWNJMVZR2Jz93gTP85idmcUNPS5cukoQVkitph8fcGfrBvc7dzBYIuGg6gssoKmEPhXXUBiDVAbHTNjbv40M10C4JybPDx+rS9QTyKOKHKVwHcnKygq1WpXbN28wSXI812OmUefs2dPs7banLb6HG4BAIJHTGYFT4raPCdfzIBFYIejsJti0IIgmZPsFmzc04WMuwndpNJtk4xjHpmxvBoS1DrO1gPffLHj/epfnPucx6vjceycksgUXViyLp2Yw07nTSeiUzfX77K3fI1KK3VGf3Sxj2B/hORKyMZVai2atymQ4Ih8lBF6BmvRxZNl28hQ06pWyz+v7qMDDC/2S/HQ4KPsY2fMQZAJ/BjMuLcJqRDohqteZpAlpkhEElfIUqNQR5vqw8Nvb3eHOrVt02l0G/YTxaEIQBlNUzcergj3XYTSJkVLgWstMoKhGll5cttgKY8CF4ajP6bkmVpuyeFQWdDmgRz4kfR1CJA8RTP9O4sTLsEg0M4HiCy9+mjOrc9zZ2GSh2eCg26XIc6q1Jn7gI12H5x+bYbU74ebWgA/W9xkm5U234sOA1Yc79MmMsv3dB2QIGq1lCs9no1ew2xvjhzVmFxdYnm+C1TzY2KDZaDA/N4e/UGVrfYfRcMzdYU4jqKO8MYFbrsVzIfIs+eiAcaIRGMTxsGjiLMVREqwl7x+UruBaU+gCnRdYKYl8H8cK9jsHSCFpzc7ieQFqym6Uojyiu48khEOcvZQCx1En4l61MTgO+I5XsjQrNfx6k6DZYEk6/Oxf+gkyHROnGTOOg5CQZQnVSoWNZJ0sy9D5Ct2DfZACB1BKgBK4OVP3+COQwEdGITMWFxZI0pxh1qc/HJJnGdrJmcQTknFKrdogqlUYxQMOBl2sq8FYHCGZq0c0AweUwXcUvvCQwiCKnO44pt3dYra1wiQ76SGdDs3LiwmylIIoq7jS7cdxS+SNLQyZ0bzzzns8efUyolYlCsPS+Tw/pH0cvviSrcz0d4sTvGAF4BlQScGsozFRQWXWJzEO/S1BqC16aEjjARv3u8zNhzTmxoRhk3dfcclyw0tfa+G5gn/zf94iDGD5fJXGBYM3O0Oa5liE/fS7AAAgAElEQVQr0McX4nS7HfI0xnUkKbA7HGMKS6czIp4M+dTVxwk9p5SMqFeYmW0R9J3pJiFxPYnvOcRxSmEsGkthzBR7Ph34GvMIaOEvDnt0VR724aEEPDhAb3eXlZkm9VqV7e0DXCeiEkXoQiNkaeFmp9DDWiVkptHAlQeYHBzlHLVkT+zNW/AF1JRklI1xhUcYSCqRwvcMdRkxKTQxCXUnYKba5NTqLGEArhFEroewRdl7mQ5gTbm7HJ029P+PavwTTeIV39CoBgSew3I15DNPniGIPJrNKul4jM5ThISoWiMIQ9zAxeYpgUxpuDlVlZCJkgChcCnwyAVAUZoEC4kQ9sSBQG84YZRrErdJuxjQHhc0qg0uXrlEJfBpRB6eUiSjAY1qjflmk0LGnFlooFszbHV7fLBxnyeWzlAJJTvdITPaLd+0BoKohhXFiUn8cLc1VqO1xWiNLjTWGASWahBQCV3IJlRFhs0sW7fvEzTqLC4v4bnREenEcZyjE4mcwvtK+rk5IsZ8VCiT48rSUFgJyK3GCSPWzp6lGlWYazUYxy5RZGk2m5giByx7e3vE8YRGo0G702al5ZMbjaGkMJvpyeCQpHHS3GhrsI0z6xOnCaP92+U9SBKGZr/Ee8cJjnQ5u3qeerXOyvwqg70OCkGrWmOxVsWzOYWwuE4TLyg3MJEoZmarvLl5B+UanNnjIXWWh3RoC2hKqOBhjhHGEHk+i3Pz1MIqaVY60FejAIXBdRVnz51B2Jy93QNGw7g8zpeclJKWzYk5C50XqEQTJpaWkxLbgIO2T9zRNGYkOgUpPbo7u+hJTjWcIc4S7t0YMEoEL/3UU9z+YJd/8+1bVCqWs+c9qssF3UDjpLCc52TaUJzwvBSTpER+YLGJwSksRggKY8hSS5KMEZlBKY0XOlTrFXxvAZ3n5HlBkmvypMAWBtdRFJMJ+SRD4kxROoITHxZK+Pchnf5hAVcOMY2QhJUKeW4IvJAsTRj2Dwh9VVb8stRlMpQ/XwkDzp1ZZWMvJkn1hyB9J6JTKOGdkXLYzy2u0PjWMrZA4GBkxijJwCg8pfA9+MLnn2F2vo4VIfWZeaxUJdxRTAlgYsohwJbtzY+LVeeT9thMOzRmZ/Ech1Zjlq3dDpHv43sOlfklcBUzjSamEIg4Rzg+Sxcvcu4JyZn1NkH0Fu++9x79XoobRoxSwajwscrDCo0jNFoYxAm9tr/0y/8xeafLu3duE64uc66wjMcj+vEOvdhwf09gpsednVGPm+0H5ZvASLQRbDzY5ubdu7y3uESr2aJRa+BuZzTCp1hYanDl0jlWZqsU8eDYdRSmFP3CWoQpyQqHH9YYxgbGgz5bt+4QpxlPnD/Nm9ffQ/gh/rX3eOLJJ7hy9Wp5RJsmcXjYg3VdDykVJ81Iit4Wg9jSdQRR6KKUw8L8LGfXlvFcp2RiHhgc18X1FFrBYDJE24KrVy9Tr9bodnY43Zxh6bmf5UF7B6nvQqqwQqOswRHqROyBoyzr92/hBj5pXjAwtmRvJqMSBeJYEpPw3p23iTyfQLrMVpucCUJeOHuWmTBA6RxP5IxrKfMzNeRen51Knfc32xSe5c7G21zfyPh7/N0TVlNex3q9ztOfegbfdZmMRxR5Trfb5+rl85w9d5rmyiwXL63xwjNPUGQJWxvrLC9WqVdOE0UV1lbPsbO9y49+9Br37m9gRDmPkcpB6+NvTKINJAXz/ZT9vRlkTbC0OObWTs7GAzg7v0S/vcuppYB+O+Mb//cm1vN54sll2hsb/PP/5VXIPLzC8tkvVHl7Y8T77YJz9RrWyXDyDon1MW547Do85YKFJM/RTjmHsYXGYsnyDMem2ELTaFTJCs07b7/DYrOJxFBkGakukJ5PojVSGlZPvUBtZrbc6g8RI9O+9nGxfncdx/fxgoBknFKvRqUFn5Q06hFCKNr32pw/v8rnnnucTveA1978gHMXL9KaidjvDnnv5gaVSshqVVOtRMzXQ9qdAbvb22xubeN6PvVq/aQ3BsMi49JclcWaZcZTLM40mWlWaFYDstxlTQq6vT5Yw5d+8ReQUpfaP6LANx10mgE5trDkSmELxUOUipl2Gj5efKJJfP9gwmg4wVEOm9sdQs+hWQmZn6uzsNhi6fQaCyunwEpsYdBFwfhggAp8Ll+9wtrFC1x8/Szf//7LdAYZUmbkvQGpqKGtwNp8ii0/Pl28+/qriP6AzBRcPfs8kRexf9BlnCRM0oRhajGFRec5g16fyWSC15hhPIkRjqSxtsRTCzMYLGmc0ht3scbyYGPMjwYj3nznGpdXFzi9UOeppx8/8bocVWYPMVDlbu9IbBjQXJpn/GCL9mCMsIY4mWCsT6/fQ6iSuaeUOmqjPPr6S3Go42/z1v37ZIWDFQLHNczNzZHFE6IopNlsEkXRkTrgoW/mqdVVsjQhGccUeU6zMctoOKHRajHXXCQIKvRn+rR3d9n1tpjkiuIEGYIoCsiKgtwUaFtggGqjSRAEeJ7LcDQmLTKU5+JIgc0TzkUNrtSqnA5LsathmtE3Ga/s3eKxlWUuKo+7+ztsjmO0UlTqHqI4/gF5dDMsck3gBTz+2FWEtegiZaezz2cef4z5uYhUx/RTqLmKNM+Rnke1ViUMfMKwiuspTq0t83T2BNJ1eLDZLo/0Upx4YoxHPRZdzeWlBu+8OyK0deZaKf2DMfGkQsY+80sVHtzO2LmnuHt7xItfXeXHf7rOqF9QrUoymRIfeDz1XIV26tBPe8zOObRmFB4QOApzQpsrjCKiKKLfH7B0doWsF3Gws4vUBoRkOBrTqEYIN2A4GpADgzjGVyVePgwDEqvY73WYmwn51ItfojrbIjdFWd0fMXOOj9/71y/juB5BGDGOYxYW5tnf3weruXzhFFdPzdDujrl06TS+K1lamCdN32J9fZ1W/TyRp2lWHbAZq6cWGc/kPGh3cf2A0UGfH7z6NmlmUI7P3/u7f/sj12EBKwVWSfrthKqjGPb3cGZjnGqFznCC9gTD4YCzK4sUO+vEcszORNNaXsDVBYuBxAjFU2fP8+07N+lI8XC4eeJ29uH4ZMk+yicxgBaM2iOUFLjugLn9LlfjAT/xtZdotBYYjcZkxtCqN+gfdBn0+mxubXH63Bpf+uKLLC82+L/++TchL7CRpB3HKBVRoJDyw73yvyjiyRiTJXxw7y7jH83gKZdBPMGtVhkkMeu7Paq1Oo5QJeNOSjb32mxubxMGIaFySUdj8jTBFqW4UK1SIel12Nls091/wP6DKulj549dx5EeDFMm3/R7hxRuaw2O47J44QL1RoP2+mZZtWtDUkyYDEdkaUrNr38Ii/zosdB13RMFfW59cBMjAqJqhUrFZdjr4/s+yyvLeJ6L53l4rkcxVSE0U1XDKKoQuD46z4mCCEcJ8mwCNseTikoQMFurszC/StQsGIz7x66jsAUFBZnJkG6phVHYlFGc4moPqUBJS2HKE5fvSJ6am6NmEhqRS2Fgay/hXjzgje02/bHGP3OagXAZ5AU6G+JWqmWP+4T7chh5lnHzg1usnVrF9xRpPOIzn36K+sGAYr1DcHYeISO0lXhRg4b0cBwPnaf4QYSQiixJCKohq6fX2Ov0iCcZxhrMCSqX1qQIKdjfzxgkCenEsObUSLOEvf0JZ7IIP6hz5/59NncSnn7yLO+/fR+dGYRQDCYjqk2JGVc5tVbhuTzH3lfMSEstTbHxmMIVpOr4zVUqie97BIHPCy9+nvqDTb6/+51yIi5AOAGVmXmMG7A/2qdIs7IIERB4HqHn000KDhLDvBdSnVss74EpPgaW7GEsLC7jK4/79+7TiWO294fEcUyRplhdcHGxzvr6BtY+j7Gaosh5cP8uw0nMpTNzzDVDvvjpq+zs7GMLy2icYBDUaxVsEnP5zFneff8m/dH28e8PLAaJEh5uYUA7FG7OuChgENO5t0dlsY6jE5a9kKp1GZuY4SQj34Qst7h5ytOeZBaNK8s2prECbS0ag5b25P7jND7ZJC5dQCGFQnjlJDiWsDvuU9veYmH5DLlOcVwfk+bko5SwWicK63T7XTY332dhMePylWW++qWv8M67b3Hr1l2yvX1UuIDj1bEU2BMgU7njs5f3ubF7wPWvf5tapLCuQ4IgEYKdUUwQRCimsDBgnBX0x8MSWVAYbJxBAcJkuI4lCjw8XbDSbHJjc52waLD14HgUhDEPazFtTCn5ClPXelMyD7HkeUG1OcOk2+NBexuTF0zSnF63x2g0olarffh3Toe8RVEctWeOi8EkJqz4+NWA5dVFHMfF9z1q1dp0AygJGRh7hDTxg6gUIXIc3FoVz/VLhIyUeMpnEifkSQxG43oeVamoR0vHrmNSpBRWg1O611udkRYjHMfF5Cm+q8h0SoGEQmITy1wgWVpdIXcFmw+6jCz0MUzSAuFH3Ol06ccxaZ6BUPTHI/KTsoZ4hDovJAfdHjc/uEESj/AcePrSKW585zuI8ZBzP/1F5h5/Erdew1Uuys8w2mLzBMcPGI3HTLIMLSRBJSIIQ0bjFGkt+gStUSks+/0J8X3F3GrAwgVBLj0ebGkc1yHwZ/iTl4e88c6EyxdXqAQuWb9ABWBkgus79AYW7Y6ZjAsunVWooIJMGpxyK9xb3yRtzMLM8do6cTyevnbB2atXSByX72YZrrVUfY9gcYlf+E//DtKP+Obvfp0Htz4gMBbXc5hfXeH8E09z/b0bbHzr22TDjJ3trfL1TQlC9hHm6nFp6/mnzuE5HqvzEa99sMHOXhccQYHP/HyLahTgKIcg8LEYPN9hplZhZXkR33WZnWlhDIx7fW7e3+TVdz5gtzNifnaGxy+v0O33ePbpCycyJcVURyZJM87WNFiHJNGE2mNvMMCEgkoNZgOPVj3Degmv/2iPu7ttlLZUHI8LrQWuLi7yv67v0MMnCmASp2grmGvMctDrYU84IR3GJ4tOkbKE/wkB0sWKcpeHGt1JD6kC0nhCNYqoOoKDW+v4M3VMI6JSjYgnbeJsC2M7vPiFz3L2Qovvfu/HPPjmH7C3t8HM2pOlscQJdOZMw8EkZW8wglzz9OXnmFuY59b9dV55911Sv04yyrCFpsiycmhqBEYYNJTawVqAkQhydBIjU4fnnn2KJ556gu3f+h3W793BnKRZYsrjqMWWA0FryiGPtlitS/ihEKRZinB9otlZ9A3Ii4LxZIxyVMlS1KWY6OFA88/ik133+M3EDwLWTq8xtzTH0mKLKJpCEq1Fig/jc6VSuI5Tym5O1fxc18H1A6RycD0XXRiKQuM6Dn4QgMnRaYI9QV3SKIEQTqkO51iMLcgLg7EZrusSxykZFqM8FD5xnJIXY/aNw/dffR0T+yzOn2FQjJmNQhZmm/Tam4zTGGsKXM+jMJbi4zwcYvqPgLxIae/tcuniBdLJiOH+AWlaUIxS2nfbUFvgzNMrWKkQSiD9CrmV4Hq4oaDhBTh+FaPbBFFIYbso+1BC9qPCVRHjLKUIDDOJYbm+zCtvjngwTHjhqTVef2/Mq28ccHm1HLDffv8D5uYihqIA5aMzl+WWw5dfmuMHP+jy5GXJhYphf2tCuz2h0/KZr8+iPf/Ydey129gix+icamsec/M20hoatRqzjSq/9vf/G5798leQns+Vz36O3sE+5AVuEBDVa0S1Bp3tNtVGg2v/8nfKfvBhniyFkD5WbG1v02jOML96ioWxpTG/SKvmo3PDM0+foVIMmZ+bw3EcwtBFOQ5L87MEUcR4NMQNz5LEYy49do4H9zeJKg2cUUG90aC1sEC3d8DyUgutj39eDjf5vU6Xnz3t896DjMV5H6VSYiFR1QquLDg9G6KKMdfee4f1W2163SFJUjCz2GT11BJeY4Zf96r8dzs/pHX+AoW23L53n9lWi35/THEik7aMTzaJT4ktAltS5oXAsSBVyFBrvvtHL/PEY2fo7O0x32zhByHSDRB+hSwfEbgBjj2L0UPi/BbLp+Z59tOf4qA74J33PmA7G+EFIeaEh0NZQz0MkTqjMVPnzNnTXDy9hms1b7/7LsM4AStK+n1RTDWJHx78rBCl9oG1IDKEyZgNAhaXlzi7dobzK6t899Yd0tmFY9dheCha7yk5HWharCjlzRylcJVD4AfovKCxsoI1hjTNCAKfq088xszM7NGm9VDfWh4puuV5Tn6CrvnC4gILC/M0mvWpGmBe9tGNAfXQKcVz3SNNEIWDReA7Lr7vg+OA6yAdB2NylOMQBAG+7+M5CuMq8hMkcaUt6zJjNDgQVkJ0YTC6NBXIJwoVuiil8J0AUfPZmky49/o6N7d2uHT2CvvpgINJTKNSZX+vg84hRmKsJMtL8wjvhM2kRApMT7PSYtBM0pRma447e/ts7R5Qu3KVrD/k1OWnEV6Fe/fXWV45Vfa6pcu4mICBIKqXQ3Lj4DgHSEdORd7Mie/T0K2iapLOsE1UmeX9a2NefXePfR+u7eyzfRCjCoeKgO37uyy1GrQWLH1cOuOMyxeafPapCOWl9O5X2Lp1wFiOGO6HvN/JePI/+hrSU2T6eD7DjWvXSScpogDl+ty+cZNASZr1Kp/+iS/x/E//PNpotNVEjRrRzCxMRagMGl0UzK2d5pd//W/wX/72P6VI46kwFTzE4Z+cyV9+7SYrq6tUG2N6k4IwComtxFEpq8tN6KbE4zFxnCCbLrmGnd02Z89eYHtnl8d1gXAsvlJIF5ZXFyikT5qPePPaLfK0IF3fwfNPkioukTKu77F6vsbWYJfT5yu8c7/DbgINIJz1WFmYpbs35s1X38KVIWEe094bszXosiYMT+/tsnl7ExUFtOZm2esc4PsujaqPcC0nNBSO4hNN4p4o9Z6lKuE+UpRVs5ASQ5P2bo/f/9YfsLiwxle+8iJPXlnlP/sH/xDfUzz/7GWuXFhme32HhaUFzp/SeJPrNMJTfPq55/mrv/JL/LP/5/d45bWbSDc4dh1JMsF3FD/15S9x+9ZtTGHpdrrkWUoyHLA0P4spMrIkZRQPSOK4HOxNqzOlJNVKjSCqooKAdJiSHOywuX6XxYV5Ou1t1paWOL2yfOw6cm3wVKnVYsRUAEcIJArPVVOaukUbTVSpYIzhp/7yX+Zb3/gGVy5f5Mpjj5EXRamvLUvmpgUKrTFGEycJRVGc2Fs7d+E8URgiKXBUqQOTp6UUbBj6JEmC5zqYrFRJLG3RCsIwxHEcsjRDGdBJBl5ptuG6bkmoch3qzVno9wnCyvH3ZTJGeYqwGmJVUQpcCYnru7iOj0ST6hSdpaTJBDLLq+sTdpMhQWORje6QYW8DPEUWd9m0AmEluS3AEVQqDo4U5Fl67Do+5AaDRTmS3qDPKz96lS99/nM8/ZmnEMbwe//iX/Iv/vE/4vkvfBaZZfzU177GcDJmd3+fSrVCeHqt1E/JUoYHHTqdffrDEdqC/Rg4YJtplOsyuzzPb3zvFsMD6KSW4EmX3TzhiYU1zPoucbLP0xdqfPEzi5xe3WM8zBiOLeOsDYklTV1OnaswGNcZi1XGLZ8zSw2Eo6bmA8ejZP7oX32TeJwilct/+7f+FrOu4Ikza/zq3/+v+OJf+1XiyRgo0RWI0qjDyFLFU06Lncwk2ChklKS8/sff47P//l+hcWoJco2QD6VYo6j6kevQwRL3djNMp1PaBdoODppf+KlneOLcEnseDJL3eP36JpXoHN/7wQ8JnXnubo1orcxy5+4Wp08tU2D50k9/jX/17Te5/u4Wxm3hJA7WFogYTupjuEqxEAo+f6HG+SdOYV2HP32tTSiqLNcz/vpPLrP26U9x4Ymn6a3/kE5f8pvf2OHKS5+muLnB7Xdv0g0b7FRqPB+NWMpjPidyxNI8v9nv83c+/wX+QXfItft3j13HYXyiSdyZ6gRIJZHTrw+JFUopNncP2OmO6I236A7/iO3PPM5gYtH9MS9//23efusD4vGYqOLzn//Nv8Jed4DnKVbOrlFr1PkPf+2vU2t+h++8/Nqx6xgM++RTXZHu/j7f++7LnD61wLvX3iYejQirLhKNq3MCWdBq1Zmbnz8iehw+e9Y65EJTrdcwnsedWzcZjEa4SnHp3DlWlo5XZfPFCGUlhdYkOQjh4ogErXNyFEJ5KEdQpBapZih0TDyK+aVf/Bpnz52h1VpCyhApJJPxmG6vh5iyOLVUOE7ZDz+JvKCUJE4SgrBOr9/DdV2sLQWr1FFCg6LIkVKSJAlRWMLSsizDUQqd5SU8SimkmpI3jMVRTmlHZk62vfJ9Fz/0EY6gsLaENlqBMQV5LlC+Qmalo4+jHNyqy97+mNyRYAy5tQjfR7qCmh+AgTzLCaRXWmFR9pld52RMMjwccGpjKExBkmX4QcBkMKFaqbC8vMbW9h7PPPEsb73xCnudDhbLcDRknExwfY+K56KzhP32Dvfv36PXH6BLEcCTHXWmA2QrJfUFh1GeE9Us1aZmwXXYfL/NSlghGY159plZLqyltPeGjHspeaYQlQC/5lENHXTVZzgweJWA1cYaE8r7YazlJPb76qWr3E4Lnn/xC7z+h99CKYGVDlGlhuO7lLLqU29cUaaUR+EFxoBwXN5+/RqtmUXG/QMoJniei1DOVA3xYzA2hQIpkTpH+j7kBcvNCi88dR7flWido3XGcDBCJznPXDnPt+5tYa2LTjLSccr63fucOrPKD159l6tPnWV7mPHW9Ta5tSX3Rhyaenx0+CJgnGju3dnH/uyn+cJPv8BW5+sc7ByQ9DKe/rmvYOyAG2+9yo03PmDzoIoK5vmJF18iLf6YW+/eoKj61BbrxG8VZHlB6AUsrZ3Dv7tJdmqZvSxFq38HyT5HpJOpihgwha6Vym7Xbt4nxydPDKOtLt0//AE5PsoJiHNDvJ9gkfTiMYORQ2PuaTY227xz7U9YXlrimeef4cmnnuLazY1j1yFlSZltVCs8+/QzFBqsiXnqqSe5dOUxMpOiJMSjCfVajVPLy0cmAEo5HOI5QdAdj5BAqFxya0jyjManZrHalmp3x8TOneuEgUIKQdRYolmbpxpUqUQh4HD/9nv4KqBSmcHLC8axpeG3qLiz9HYz+t11At8nT1M2Nh7QaFTo9XfpjftcuPQpomoT1/FIkuMrz9W1FuNRApSCQbVqDeUofNc5gijaKdj8sFVjp96P1trp2FPiOu6USm1Al+MqV6mSfWrMlPjz0eF6JfnDGIN0RMn6FLIUfZISx5VY4+AiS2liDKoa4iuBdMvWk1eNQJiSeSolVmuEdDEGBsMhAos8AYN7xASHcp47/TyJY0bjMZFfwVM+1UqDq1ceR2iBdH1u3rmLFwR0ewfcu3eX1uwtvvz5z5COh+y1t9ne3iZOUrRWWHsyneMQtaQcxWwrREiPVGiCCCpWk7Q0ndTg4rG7s82GW7C4+hzVaIJWMbaa0NYxDw5iUukRLc2i3GZZIBRgyNFWcxKa61d+/W8yGYx46Wd/ml985mlqrQaj0YQ7N27worUcCRodzhH+zKvIs5Tu5g6b79/h6S+/xPtv/ZB7H7zP8qUnKGQxbbycHLk2OMIQ2pRxbknTAqsF/X6XvYGHF3q4QpPFMVmcUfNdOpMOc7OnGByM2NrqYHTKcJyy3e5y484DHrt4kdF+j7vtCfn0mVYngEKSIqVuEpbrTc5fucx2u839u20ifC5dnKV5+jmu/eG3+O7vvsrObsa+HfPYi0v0X79GY29IzQu4cf0Gf7S+S3vzHhuex35rhpnTq3TihP/5//1devt9/GPN6h7GJ5rEff/hAEW5hzTX0vm5KEoUsfQqCFysEIzzDOUJpCl1EoTyy8RoFN/709cYJBMqtQZvvnkLW1zn23/0pwi3ymByvN1UpRJhtUaGIa4MGUxSLB6Oa1DCwQhBrV4rzWmEBGNJ8gzPLWFWMG2KCYNfr2G0wbESLQzaamShoHhYsX9U/NWf/xtYaxmNRlx7603SPMH6IRv3d+mP9xjFWzgI8txjPLI8dukqZ84t8vqbr9PZ7zA3N0+tXiNPUm7dvcfFCxdIswE7exucWr2MF9VRoqycj4szq/NkmSbPDdJmpKlmMo7J4hgv8Mpj7rS/fajKp3XpgsKUnFBqWRzCJA8Zo1N7tCQhy7ITB6wIS56nFLnGi9SRuJfAImXJvozcEtWkjWGcjxBeaRnn+R7OlLlqsUS+wuqidMOxEqMljXqVLCtf3/FxNHUr9TqmST9OYkaTMdu7u0zihHtbm2xubvL29et0B51SG8stB2oGQxCG7O/tko6GdPd3mYzHaG3RVpb60SewsIwsM6MjBM2ZJkGYTVuPOXk6YWFNMwxietcKXrteUGkuctmtcPtBh50gJZMuB6ngjffbnDvfQnUhqlp8v7S4i7MEPTUfPi6+/HM/gylypOshleILX/0Kr736Bh9cv05/d5/aXPORHD6FuWKnzj4OP3r5e/z+b3+dldYyX/ylX+T2rffZ3eoAZuoBcMLtmEahc5TJWKhJuki6RQn2+8YfvMIrP67x733+cRw/4u3rd0mGfZZmK0yGOTeGu6Q24L0HB1RDj+DWHlfOneLO+/eYTHLGqSROMwpddgmcE05qNV9wOvTxJOy98wq9gwGzsw4rc6cQrsfdl3+Xa69d5/2NDsLxSaQizkYMhvvU6hFe4LHX2eeVdo8HFhzt8N0PPuB7N+/Tbe/R29/CFgr7Mb0+P9EkXhTFkZbvo/TwI79C5RyVQUIJHDdEazBTckb5QAl0oeiOR9xab+O6PQpRBaXp9BKQQ+wJFXCWp+RpRuj6SGFI05QgcogqPr7jYYUq1Q2FoMhyjC6Q0sUiKYoSUVMq25lSmU+WE3Zhy6riocnzCQy0O3fI05j2zjaj0ZDeaES312eSHpDbfTwf8kKSxqBzj63tHRZv3GI4OODc6hrWQhBWcZVL4Lv86Mc/QjkWP3DIco0pNEKVVmXHh8RzFZ5ruXDxFMkkpbPXYWd7n+17I8ypJV/n3dYAACAASURBVFRrthy0yrLCLUyp8yKEQBhDaZRgUFZiioIsS0iTmCSOyfMMrUsT5ONCFzlWlvZ5QijAIIQ9slwTRhA5EVFYwWLYHRgSk2NMhrAKR5YVrhXg+UE5qMoySqkwl8D3SsKOPn6Tf4RzxWEiF1gKrbl15zY3b96gPxwyGA7JpsbLjlcWIw4aYcqWjdGW9s4eOp3Q6/fJ85Qw8JkNInoHfZL8+HUcGp1YKfC8sngoNV0UWINXFyzN1ei4KQ9udHllPef8/hvcvLXHq4HlTU8w2/ARueb0OUUlKElbWZaW6pxTT9qTIKgIhZY5EkNQaXDl+c+yl2jSiWGwP6Q21/wLLuL0QgrJZDBma+M+880GK5cvsvb4p2jNraInBSqUR3yJk0IIS6sRcGmtAZU6+0NNIAyOSNnd3GF7q8XZtQVOLS9COiSfHLA8t8Qb93tMrCEvYrz+EEfnVPQY13NY39fsxaWJsTiUiZDH5w+3sMSpw+3tmNuvvIN1I7LE4/tv30dkKat2g702dBMXN4DaXIW88AivnEUnGeaVV3AQDNycqvLQSca9O5sksYY0J/MzMsc/UZbhMD7ZJK71Qwz01CHEmIfkFF+U9Nvy4VFQOLhCYZ2SkiumR1vh+dzb7mO9FhkJ+GDwkE6ENRmcwAxMJgYhHDJTJoeZucYRq1GjSuC9zhHKwfFdROAdVZ5GgC0VGDDaPqTJH2lPK7QtJ/UnqZD95j/7DcbjmAIwecIgyWjNtGjUAoRssrXVAwyB7yJEwXjSYe+dLo6SdHb38D2XwHVJxhPWTq8xGR4QFymNKGL73h0Wl9eYxBMm8fFQx//xf/onVGsVFhZafOlzz1KvVpltVLl07hTGWB5sdxnHMYODGM/zqVarKMcjyzKEEHhTJ/U8zxCJQCmHNMtI4pgsidGmwFpDmh5/InB9i5Q+FT+isCW6QzrTIbgCR0ZoA8YtkBha9SqxMcR6Ql6kCA11v07kOYgswzE+vqyQygIVSNApNd8jOH6+yqNzDzlVHjzEWty8cxshnXI4LyQqDFFSlgw+Sod6aS0Y6O4PiA/aVENYWZ7luc+eYX7Gx9OW3V7M6+8e3/azaU4uIBMGT8oPbcZBUCUSEVIIWk3NucfOcOf2Dh/kmyyvGF4wEf3RmFQbVs6ew3MiisISRA5aa8bjMbmw5Hn+sRKokooiyfnl/+K/5tmf+XnOvvAFfusf/++8/J3f5z+4+Ks4bgTT7RJACI3OMgb9PR6/+gS/9p/8bSyaa6/8kMrKPN/9k9+nWjecuXqZxsrpI4GxY6+HcGmPNN/88V1M9y7NiiQQMYEfsLyyyltvv8d4lHCw2yHPc6Tr0B4NGCSaZByjpGDshOQiYCgaTGyA0QXK5BiboJRf3lN1/KaWKofYChbrPv/Hj0csRTlWatqZ5nJzht/9YYeoWkPYjNFEUux3GBqHf7T7O2itGU1SgqBKYApGlQztZzT9gGqzwu2dbX7la1/j9/74T+n3jpeyPoxP3BQCcXh6KmUwlXIeUuWlmsrdMWXVTXWDRemiAkyHDhYtXDAl8kIc2mIpAco9Uir7qHiwsXX0N69cvYw3dWEXQqC1psgLwOI4hzjoP896PITyGVNauB3C8D7kjXfCsyEdRYYhnkzwXYfA86mEEVIJ4lxT6FK4B1W2D6woP6spq7NaqTA/P0eeZGRZiq88pAaRWvb3OhgzbUOcIC40Gsb0+yMePNgm7h/w2OVLnDuzRq1WQSi4fPEM1lrGk5iDgyHj8YjCOvi+j+s5pLlBmwJJiUrJ8pw4jknjCbqYOnnr4sSTSZokVGpRaaeW6rInnmX4vo8uNI5r8T2XSjUEo0ljjdQSxy2F9JVxEIUhLRKiioMXhLiVCgfJgMzE2DwDJU88ETzUqhYf+pqpmCzoR4SYwBqBdNxSzEhPT4toxmlMs6W5fHmFi+dWqFcdHDL6e/v4Dly5cPzg22hNogtSYVDSfWjMTNkiUo6LFA6+gmo2YWXFIb4bMRnGxOTML1epnl9hYWGeSrXGeMpbKKazDG1L/RP9cZx9jMbxAzIsk2E5vP/yS1/EMQWTfh/lFnhBDcf3j+YJAnBdRXN+hk/NPY/WhuFwxPkrj6GKmJlmlajawphSThkrjle6tAItAtzaIvNBTmBz6oFCCs1onNJP9rFWowIP6XkU0mG8G2MyDUnGJB1SBE1qKytk2pReQ1IBLhKBMRYh7J/RKv/z4boBQmhiJAeiSqAtCxXNrC2QKuJAZKSZxLoBRWHIZYCrHCZZBgi8KCrNZtB4XozRLk8unWZ+YY4YS9SPqVYqjD+m3PonjhMvE7KauvDYqTt1qZWNlIhpa0WqUjDpkH5+5P4spz1XFSC1xpjpUMXaEiI0rYaPizTNKApNURSMR2Mcp0zGSinyPCdLYxxHEUVROcGfPrCHx75HWZFlP1geff+QJXlot3RceCblzFyD8dhl1BtRIHCyBKVBZgkVJfFdB2+KpZ2tVmhPBoBBJwmJo4jDiDRJ6PZ6hEGZ7FKdEecFeV5gLYT+8e0UK0oVOWsk196/z51726wuL3Hp0jnOnD3FmZUlHCmo1UMq1YgkLrhzb4PBYITn+URhBTxwlEuSpqWmymRCnhfkeY4uCqQU06HwR4fvl84+qc5RU4EoYzWOK3GUR+B6hJ6LtJZCl/6EgXDxvYhC54hC4gufwuQIK6jWaijlEcsEk2ZktiQh6RMAuPJDOPKy7y+EOKovsGbaKZBHaodSPJwPIErqNELx+JMXuXh6lmogS1VCqRgME9Lc4KkTdN5tOUD2PYWrvIedenPYxitPtYUB60nCikdbetzKMzYnmic/f4mFtdmjFhNSkGcZWZ5RWMPYjrHWlG5bx8Y0ueYJvc277N1bpdZqcPbchZJ1aRRTw8RHkrDC9UNc36fEgpernymbT5SQGAO4cOKItwxXgVKWPJ3wwd23CZVL6Ie4KFxHoTxFoVN0npPm+v9j781jLLvy+77POefub629a+mu3kl2N8kmOSRnOEPOouFoZGlGthXZgCwlTgLLQCIYhrMgdjxOYCSId0NwojiRgkgy4mixFstaRzMjz4w43Nlcm71319K1v3r7e3c75+SP+6rIEdxVE8AgZKB/AEEU+PjqV/fc+7vn/H7fhUQb2q12weA2CVY7+GFEEI0XOjfCkBmJNhYlZMEkF3u49XuH54f4jiV1JYERGNdDhbAQSRwiMqNIbU5QdtCZwYl8ypMTVOsVpFT0ugMMAiWhrjUtnXKqNoEMPSaOzrGRZvhjVWbKlQPz2F+d/z8OEvfjftyP+3E//nTF98jOvx/3437cj/vxpzHuF/H7cT/ux/34DzjuF/H7cT/ux/34DzjuF/H7cT/ux/34Dzg+UnTKo195ySbtATa1pN0BRuvvUqIUI1SAQHwA2bOFQ7TyPdwwQLluwQYzhfKEoxR+1SMoRZQqHiP1Vr76Vx+454i51XzHIgRKKpQspAAKuJgsYE57zi6yMMM1xtBPYzrDhHY/oZ8alJKMlzxm6zWsEBiT7n+P1mZfRXBi7Ow98zg2V7WOL3jw4UXGJiPSLB39vpxyZfT3IEmTnDSxGC2pTwTsNDbJs0KjRAgNVuA4AY4SYB1azYRb17bpdjRKFQiKa1dv3zOPzz3xuHWiEOsosjzn7KljlMsR3V4fIcCRct/+LU0TtNZElUqBSNhzIxkhdpIkRuucXOcFAciCcgqilLGW//sXf+WeefyV//zHrbWGfubw//z8L6BHWi1FWH7mp3+BP/jav+Z//sf/gEcePHugvVmBJDJI5XL1zTf4pV/835h94If5Sz/+eaLQQbn+PfP42Z/+KVsKqoS+Tz2qonwXlKQyVkeblH6nD1rS6/bppz2GSczW1halKKQclJiamkbbDjdu3SHuDxmfmOf8hYu02i02tlY4c/oBjBbEccIX//xP3TOPv/ubP26HawElX+CtzzI+VyM94RCKELkWMhgMWVw4xmDQwXddrl+/yYWHz5GkKdJaJmo1bm1cY6n1JpE/D90yCwuzbOwuoULDbOU0gy7cvn2Tv/U3/7t75vHQZxftqfMhFy4c4bd/9TrlWok4yTl7YZ71jQ22bnbpDySOX6FeyjjzYIkzD43RGyS88scbtDcNSZIR1V2GTZfBIAPjIKWDJSdOe0hZQPsat3fvmcd//8/+L7uHCttfZ8QHdWOvkNiRp+bon3yPzPQh0TEBGNS+lrndU9AcEfX+wV/7y/fMI8+wxmiSRON5Do4DCDNCtrBPntpDtH3YKWqvru3Fh5Fvf/IzI5euQyk/H2kRd10H47toCscaIwp44R7zdi95u6dhDYXmsrbkSYY1FudDLiTGsQgVYgYpmQfaBAUY75A/O9cpjnQROPvGqaNvZI+hByBMoVRkTAHDCjyPVEOqYwaZYbuXMBYUcESD2V88YxiZFB+m0VGQjpRySNMURoZdYeQTRk7xMjPgOC7WWLpxjzSTI8f0vNDgGNG3k6TAmrsuBIGgUnNp7g4xRu3ji++ZRyUkl6KQjBXFGuR5jhQFVjmIon26vXIdsjRDWouQoljHETzTWkscDwsy1+gluaeLo01+KJDMd12SPKc2Ple8oHA+tDaFqaxUPlFURUiF4mCCiLUSoQRHFuapVeuMT9aplMNDVfseOn+RPFZ02husbi1z/ORJUgPLd3d5//3bbG9u82d/+HnGSz5Oz8cdJozXK4S+SzJM6Q2auFHO3IkpNm9tcfX9t8jylEceeYI8n2FpaYUgiMizg4HAYhBSKhmM7VOrVIj8CjZN6Gcd5MBw6tgJZo9M89bb6zQGA+bmZun3++Ra43ou7e6Au3caDIzkrdtvMtg2VMseypd8/ovPYnLD+NgYM9NPH5jHw09OUZ6ICauaT33/NK2dgEuvrtLYbnLyzBjNFY1KU6QbAxljtSk21raISgFzs2X625vIQBG4knavh80NRgsGwxydQ56nIz7IgWkghdjnd+yZJX+Y6i/3GgtipEQ5+owcPYd2n0gI2FEbQuz/yId+PDAGg2SkQgnGSCxyhJ4u/u/hcEir1WJhYaH4zj9BZNqrC3uF+sPxXS+oP43OPsoRKK8oPNJzQBeM9b2dtzFmZFs1coA2duSkVZgkGGPIR5JryinYYQJNXeS4riFJc5QaCScdEJ04pxx4SCm+29uSgtq7R/oRomB0WmvAapQU+J5DkLnEVpNYSzvOKPvFjvcD/Pj3SiUuSDuO41CueEhpkBJcT+E4Aq0/oMdYMjxfMxwOipeKMpiRxyGjF0gGBTnJt0zNhCzf2SHNBOoQkXttDeVcIkyOXwoBgaMcMpHieO4+OUY5ClSxu0HnuK4zOgUUQllKKXw/wLP+/s97uhwSM1KBvHdICXGqOXPuFHvuzvs3PBpHupTHapTLPpiRE+EBl3hP/kC6PkNbYmJyDKTAHuKx6XohOh8wNjlOEDp4UUS/k/Pzv/DrXL66wbFjpzn1UJMgSnnpO6+zsrTKf/GTf4GwVMbxM5w0Rrp9BjsbYBTVSsjS7cv4nmT+6Glq1THCKCQeHqzjnTQdwkoXrQZMTEyioghMh3jQxGqHuelprl+/wtrdNYZxjO+FHD9xjDTL2d7eYml9k91GGxl6XDj7GNfS61y9fJXzFx5hpn6KyXodne9f6nuviw3xXOh1uzx4bobt1ZCVGz2G7T7KBkyMVYjjNawY4jgBGxsN1lf6RKUYmwXs3M0QSPolTb+dI6UiSVKyRGP16GWNgENs4uyInCSswFo18iUocPoCCrrsh1jfBX3EIEYcEzEiGtriy4rP7om3WBCYDzQXDoj8Q/r8WZYhpUI5H+ykAa5fv069XqdSqWBtwYz1PG+fJAjf7cL179qNf6/x0aoY+gqjFRZLkgkwzoeurAVjCvPVLEdYibSF67wElKUo8MIUxycHhHCIZJNPLyRUJyf45TudgqJ+iPrXlbtDamHM1HjEVFgqFlN8sAB5no+kU9kn9Bg78tWzksiTaHJ6KSy3E+q+YKLkErhqfzEkEg6TPBUG1/OIwgpRqBCqcAO3dqSvTI7WoKRDEEqCKMIaxfZ2f3R8y8mNoNNJqddD0iQFJGHkEpV8zj86x+qdFq3GwWYMtahE6LuUSyWiKECpwkwiCAL6gyG/87VvMzs9zqMXHqJUKiGtJs2LG9BxikLteg4g9gt3GEX7etwm13gIhsnBaordOMOqMs9+6hm0MfuiV8XFEOSEfN/3f5nAK7G80WTuyNghK12sZ78T0+0lnD2zAMZymPPT6soVolLEWH0G5U+zdGeFSj3iz//I50l/7Y859/AT/MpvfJ3nPv0Z3OoZrizd4Fd/81v8xI//IIETUI8CpCojiVg44oH18H2fxu4md1evc+PabdqtPlrDl/7i37hnHkfcBdabr9GRAzbzLZItyfb2XcpVl1PHZ3nj0oscPbbIx59+EtcLUI6Dzi3CKkpRRGmxhOP6LC3d4dmPPcOXvu8H2Nra4evf+CrXr1wleuQilWqZ8BBtnT/6V9eIsxg9dLDyNoNeB9eWCL2Q977ZZXJCMD1XYuHkSUxm8ZyU8w/WmZiImJmYYv4//jy50QzzmExoEBbf8ylHAZWSQAoXJV2Ed2AaSJMX7YcRue67dsCCQpRupKrpKBffVSTJEBeLtgZtC0IPsC/RvFfw98TcrDhckSvP832Sn1IuWZbjuN5+/YjjIb7v8+qrr3LmzBmUkkipmJ2d3f/dBdNbFS5hf8LcHD4o5N/LbvwjLeLSkTiORDsC5cr9fure61FqAbJgXFpjCsajsEhRXGyMwIjiGI8USAdcFZP2dwkmqyhhkMoeWjtja7HDFNlVlB0HRxXaGBaBtiDNPrm0SM9a8pFAsrQaX1jwXASGnUzTzRVeJvAci++ofWXOwwyKwaCUQOt8f0ddtFgYFWlRGD8LAyisKXRKJibG6XY7ZFmC4xRUdyEcpNCjXYKHVIb6mIfVY6Tx9oFZHD+2gKMEruugHEUSJ1ibY0b6L8eOHqO5s8WrL7/K4skTLMzN4fpecTyVElftPRj5vriZ4IPeoNYFvfmwto7Wlvr0FHOz03z3s1TcIM1OnxNnpllaXidOM47NTxTuS/eM0S4+bxAPtqhWo9H1PXhdonCMSqmK1YpWq1NIIWCISornnvs4rbbh0UfOc/P6Ve7e3SBQMX/uS88QSIsShX1fXtAokZTw/ABtcsbHppkYm6RWnWK32SDPDxbAOj57lu6dTXKxjbYuQeiysHCUwaDHxOQ0Gxsb5FpTLtfo9foMhkMqlcpI/VGQZilh5LC4uEC7vcNYfZxqrca5c+dQysHzHHZ2NqjWIuDcPfOoeiUmKhFR6HH+4kmkMfv+sxOTNSo1y8LiBBOTZWaPTBPVFaVIUAkiPBHQbQ5pNPokiWHidEhQkygrcKyLsQnG9oqVOkQOodfr7xc/gYsYuXTuiejptL8/jyqVIlxVQgqNRjMY9MiNwAo5oteDtApnpIMjhSxqilKHttuK1mfxfEdRiOsqQGJGQmmX33sHx/FYmJ9je3Od3cYOi8dPUquU0TrbP6171QpWfsAI3w9b7NLV96h7/9GaQjgS60kwEuMV7Yd99XghsJnFaIGREmtAGoPN1QeekWZP23nk9ShSpsuGsdChHqp9Wm7heH/vyISL1gbby6j7CYHroERxvDIIfDFa1D0tQgt7u7pCeRGUtUgkrSEkuaUzzAmVwncLOdQPdezuGdZaHFeNip5CFGK8++p5Al3IyQiLknstGkMUBWid0+3pUf+zkFrdK3ZpkuN6IJWhNuYzPhkemEfgu/ttjzRNil6do1BSUC07dDt3ePaz34+0mrWVFapjR7A2IRn00EbvS2ZaW1wfKz9QqCx0Oor/eNhLbZjkTIQ16tXS/rXf+14Q6HjA0vU1ukPDI48swnd96t91gYt/dQeGYepQrQSFnKmyB67M5MQsSZKQJCnWaMpRSG/YoNeOWZhbpD4muXH9FotH5xj2uxybP8/RhWnyOCczOUpKorDKWK2K43g0m9vFi10qrFHMzh9lfHL80AbsoJ9Qi6aQaOqVGW6sXCMbtgn9gOvXrzM5OcX169epVceYmTlCu91iOGyNhssGS06rvYPvR3R7gk63je9HHD9xijQdUqmWaLY32djcPTCPf/zPfoQgcAkjl7nFaZp34aWvv0U9jKj6JbY31zgmJ+nf7bKyucyRE1NMHJ/FDl1uLq/x5htXaO3GYAOiYw6feP4CUzOSxHbQ2kGbIUIojBZwwK26vtUatRIL7fi9k5qQhY9sPGhijMVog9tuo80kp0+e4Mq1m2zutEl1VmwyjMWVClAo5aJUoQMklYPjOuT5wbOKvXZKIaGtix11DsIpJBus1sTZgHIpIgo8Xn3pRcrlGmmcUpwTLCdOniDPcqRyWb27SrlcZmJiYqTyUMzglu4ssXh88eCbhI+6iEtbOB+5Eu3tvbmAkcOIdAQ614WvYm4xBfBitCsb6TKMipkQUBI5c/6QKNmgsxqj3KdQzqFKkmRWYY1Dnlpag5ySD4EjcJRACAvOB8ebD0+KrRDFqUAKpMlxpCTyQCeaOLc0BwbfVfjO6KVzmAmC61KpRHiuB9ZBCEuep2jMSBYTlCoKo+sWR8csK1AfQRAUN5DnIIXL1noXhEaIQizfmKJP5zgO00fGDsxjbwhrDGRpXhgwKAelJK7vsNtoUK+P89BDD9Dd2qA+NoUsVXjv3ddJBk3yeIAQcuT8o8CVGFPI1LquQ5oNcIRz6NHQqpAHLjzKeCnkTxoFSEchcsXLL77M4597msWFp8FqzEhR7U9+taUwto77Ge+92yfOqwVySX8w6LpXdLtdhoOYUsXDcw1pMkBY6HUGtNbvsLbVptXq8JM/+Vcp1zxu3rxMu2PAZHi+HOnyCKy29PqbpFmbIHSwuRipdhocNzi0iK83b4HqYXKNW1IcmT1Ca0twdH6ere0tlJI8+ugjdNp9lJJEgWJl9Vah/5Pm5HpIf9Am05osc6jXppmccun2u9y4cZ3xsTEajRaOc3AiX/rCUxhyUhI62Ta/80tvcvXVuzxz/iy37lylHFXQfsyNyzeI3RzZB7GVEYYhq+sbxLsJDApkU9Ct8MYLd3j807PUpgyp1gjHQYqA3Bzc9ttop4U5iRAoYVDyg7vEcRTCpB8U9jilVBpihcuduw36mSADhMiQgGM1hQJNiqV4vh3pAuJQ9dG9Fqu1luFwSJ7neJ6HdCRSWqrVGm9degNXCYRy2Gm2+fYLL1KplFmYP0a1Vibc2mZycopazafdbrO+vs7ExMR+W+eVV17hq1/7Gl/5O185+Cbho0anKItVRVF0vb1pcXEDGQvKCrK0GO4ZJbBaFFriduTmbovWQnF8kvhakzRXyWjTGiS4kxYpzGGnMqwQZBqMUrRjs+8stecIg/0A7rg/mFOFwqKUo563sDgCJksSYTW91NJLDDvdhPHSntbpwQ/H9JExTpxawPXLpHFMqVZibvYieSpYXr5BUN6i6BxprM2wqJESoMVzPWo1v7h5hESnkuZui2Skb21kITJkRE65fIi3ZZzgjPr5e/05rQvRJKUz4jRmd2eL3a0p6lFA3G/x27/7W8zPz/PcZ7/I+2+9Qm93C9f1kdagbWGVlltLrrPR8fd7mLRLj9Mnj+E6BSLJsFeci+OawaXZbPDYww9QicrkRuOMhKD20ALYvd9UCGe98PV3+de/9tvEZpOXVzvUxkJmSy61A9JI0yGt9i7KHWOQ9LA5TFQq2HyXN9+8zLkLFxgkPdaba1y+dpU3Xn6HYdvgupbxsRKe7yNQbG3tMj1dZWa6ysxUmdmZKfySS9rRxHm652R2z9hs3aVUMtjcRZASei46qtNstomHKW+99Tbzc7ucOHGKJIl5593XKZdLjI2NoXXOYODguYp2r4nOLfV6DS9SiDZ0uj3eeedd+r2Y4BBt3t24V7zojWGnEXL5rQZ+XmLr5g26u1scOXcaY1r0O5tMzU1STodk20PCiXECm1D3DNZx2d3t8LEHnuDq2gbX3upx7MI4QXWAsSlWG6zI4YD2fLM3KE7IguKUSvGcKqkQMh8ZxuQEjqJaipBKsr21icAQxxm5LOzdBMUw1Jq8cPERAqktJk/BikN1vIMgKBBXWhPHMVJK0izDpIVc8Z3bSyzdvkm/1+XG0gr9YcaN6+/TbO1SLU9x4uRxTp8+wcWLF/niF7+I53m0Wq19F63f/PXf5IUXXuCJp548OJFRfKRFvOzD0FocZfFc+cEEVhRvVKENOi+OVUYb0JI8y/ZbLtbaArEiwHUF08ZwpjZPWD9ONSpRbeYIRxZF4IBwhCFXkGrNdkfTHxrC0CPwBVEArgyYKSkCV5O7EldJNncMmQBH59SrZWpjAX6SUqum1F3B9tBle5CyNcwo73SYrkV4zsFvk9n5CWYXxshFl/npYyi5SCWa5trVdX7sx/42v/iLf59ybRdBghEGYzJ8P0TgFMc3BHE/RwgIA4/ywhxpajAaMp2z09hACEliDh4oKuVgtEGj9yfoxY5Ak8U5X/7i8/zBH/wbLr99iXq9yquvvcQ/+Zs/SRhWmZmoMf7ZH2R3t0GW5TS2NlhbvoIz6GBcQ384QEpBnulDHYYeeORjPHX+NBhNjmUwKsqJsQzjmB/8L79M+rMDfvn/+JfUghrV2TqXVm6yubxB24RUjy8yUw1xPMXK2ha91RWufvV36NSgN36K//bv/w1mqzA2Nsn//l//vXvmYXTKiWPztNttvNSSKw+Bx8pSg/cuX+Wv/Vd/jXcvX+Ef/b1/ypXL16iVJ3jtzasMh32UdFDKpVIpU61WWd5Nke/vIowg9EKqpYDnv2+emekqvnuwimGr0WY8PMlOY5Ntr0Oj1UQBD597iImpMd59713a3S6vvf4CeTbkvcuXefxjF9HGUClVCH2fNC1TKYf0+n2+9rVfpTw1wcmTp3j+889wd7mBtRBFB08UJ8Uz7AAAIABJREFU+6Jb+Iv6If/iZ77JyVMP8/63X+XK0gpTvke6u8V7ty5zeuEYQaTYvnGZhcVphmbIsNnB9mK0FgR5RnL7Jo+fOsZbm31+65fe5fM/vMjs0cLwIj9E9VNLCrlfwBEehmKTZYRCWsF02efssXk+/tjDHDsyjXRyapWIH/z+z3JzeZO1tR22NhvcWV1hs72L8tS+n6bv+7Q6Q7QRH+DK7xHWWoKR09Uv//IvMzU1xdNPPw3CwZEOx4+d5PaVt7m7usQwzjn94AUuPnWc4bDL5XfX8NwKYaVOp5/w2uuvk4xs/372Z3+WlTtLfOqTn+Rvf+Ur1MYPPkHvxUe8E5doZ6Qmbp3RkVeiCosFyC1GCrRRGD3qXTlgjN0bPI+gQxZHCYJ4gCBHO2WksXjKQTgO/iF/lcEilEOep6RWgHXppQqLi+NF7DZ9xpoONlB0kLhG0+rnBGFO2gO/MeCiqXDW8/EDgVQOdVeT+xqdw1YvwXMsE5WDp/46E3iuT5r08UPDt775h6yvZDz4wFO89OLLnD51kXcvf43xyQCp82LYSTENN1qALbTOPdcnjgfkOsN1PawjqIQRSdYlTTKy7GDoFnLPqWikzW4L96I8zTB5hvIDfM/h2p077OzscHphAj+qkSmvaK0kmmarwWAwoD4+Tm7PsL1yg6TfppAnVRibHioBW54Yp+u7xNrQjDU32z1ajSbtYczG5jpbzS28hTp33t/mn/7Df8hDT57jvcEmrXabZGOIs7iIWwqoS0n7yhWyxg6mXmNQr5K37zBbEUxVM+Kdtw/Mo9tr023u0u/0KFcraJnhxTHuboO//OUfIdWKn/u5X+D21VuUw1IxCLO2II+5bqE/7zjkRqMHLVw3xFEBsckxvZQXX7vNM0+eYW7q4Je850bcvr3NoDfgzOkyRjnk6ZDtxhZ+5DA5WUWIIcOBIVU5Fy+epxyVSYYJrnRxXVkYWUsXKSRWW/qNLt2xLeJei9nJh+l2u8zPLxyYh0AThIKlKxt8/XevcOahIbsbDY46Gull3Lmzy8TMOLWJiI2l1UKn3NRIOj1MnBD3E3yviiBjdbtD09xgbGYeei6vv7TCc2NjKDdCqkOcjkY13hiNxuzLWGMFeZbzfR9/iofPnubE3AyV0EP6LoHncPXaLfrNbWbHIp48/zQ3b0/z7ZdfQiNJ0hzXdTkyO8tb79+m3Uswh+ir9/starWjGJszOzszakXm+F7RylleXmGQZAzjtEBC6ZSknRKnGpsbHM9y8vgpjh8/xd2lKwR+gCsV6TDGcV1efPllLl+9ymNPfIzv+9xnDswFPuIiPu5rrNQkuaGTZ2gUuZUFKUSowuFGpmhj0MrBWMDRYATCyNGAK8cxQ6oe1HRaTHuTIQqPijJoR+I4hwBf96A7FoZCEVUrWE8RTMywkaSs9BW/8/IuwewYraHPmNIYL+D8kYSWHWcyUry73eFG1uQ/OVWnLy2Riqm7DpkLa50h0maApXTAub3dHmKMJPDLrCyvEscJV6/f5fadFr7/bT79mc+RDedI+gbptPGDYmZQFHGwRlNAtz2iUpler1UMVkxBaDpx4jitVpONjYPRKQVYq+i5p0mM63kIWzBPkzTm937/q/QHKYM4w3VdlOMyc+wMv/Wv/xVxavnED/0E61vrCJuTW0VtbAKMZvXmu0X/UOui53gIOmU7zvnNt9eJux2Wlq6ytb1Cb3eXOEshadNJBviOi5qOuLa5Q+8bv8f27Dyq7KKzNsHKTTwXkt0GOvARR8fI/AoiTxirgu9lrDYH2O2Di8X09CS3rt2k3+swc2yewPcwKyss2oRHn77Am1/7Hc62dzkxMcY1DS1cXNfH9b1i0ItFOhBnMWagCUIH11Vk0jLQPbp9Afoqn3nm5IF5zM0u0G7AztZVdrY3efiRh2l3tlhZvUm+0saR0O+1cJQZ+YyWsBryLKffG1CtlWm1OjQa2wzihOmZGYSxbK2tYRFUguNsbW0xNzd3YB7duE2SpLzy6hU6rYxrt97DiTNmKhFeFHBzY52pxQnyfgedJ8QYMiWR1iDyAekgoRLVyZKMJIO3v/Mejz1b58SReS5deYXPfXmC1A5x7MHF02SavWO5UAZpC2ctpIMjob29zpVsyObSberVKguLRzmxMMPbb7yBH9XQOuG3f+fX2d7Z4ZOf/BReEKKNZXJykmq1ym57QOvqUtFXPyDev3KZ6ek5tE7o9boIAe12i+nRYDIIA6Ry6Pb6pEawvblByYOrN6/T6SaMjWX80de/gZTfQtkBn/jEM0xPTXPkyBGmZmb46h9+lVdee5VX3rj0p6+ITzoJnm8x1iHPErJ0SJ4mDKxHouoMXIEQkswatLEYK4lTwBqEtQgLTtJF9taYUAPGVU5gFXaQ0m1lTNXHGCIxh4jtp0ZihcQqFy+MqE6Ok0lDWHGJzIDx8Yh+bFmciWjdbDFRVbQGhpPliKWuh+jlnDg9wb+93SCxCkd2MY4mtAm1IGepI9mNM2yzx7EDNjmN3S7dzpD6uEecBdSqDqVSj053wOTUOOsbdxkOYto9Q6nqceRIBRuvjZzeU6yQIys4gdABWAdHhuQmIxkMcOSAcsmlHB0sLm/yvJhJSNCmmLZLWUCtBILdVo88hyAoLNgunjmGNLDcTJleXyUMfWqlMtvSRWLQuaZSm2Bsap5Br4uSxRG08Im8d7z59h/z0o3XyAZ9RNZDWo1SPgZJJTAICSYzuAoWT51C9QfUd9ZZmD9GO6/TvrqJFyrE9Bhx2UNJSx43yaWimxUembWwSpYfQirJDRPTU4xNT1KplMiHMSsrW9TLNaLNZcJLbzDvR1xyPHpxhqtcvLCC77l0OjsYK0kTizYpaTwkimrFJiVL6XZajE3P8+71LZaWl/gL/+nfumce3XaXdscQlcsMum3u3nwfFea0ums0WinH5hcplSKEgeFgQJ5luCrE8xTxMOGtN29x+85NOr0ec0cXOVau022vE5oqgVdCScHJkyfpdrsHXo8cjVUum9tD+kmP4wslwrxC43aHo+M11lO4vrKG2w4p1RSea8h1QsVVJNJha3ObUjSNyHPWVte5dOkOuVfnkaeeJtmJMTYn071DHbkcMTIxFgKdDRAYIt/DcYoiri3cWFpldWWV9m6T5z/3HD/xo1/m3AMPcufuBn/wja9z884tTJbz6U9/hk987HFcVyGEIteaLB4WA8tDeuJxHBcOYDrn+o0rWAtHj81zZHoCk2vGxuukacL29jY5kmarw5MXzzEYDFhZXaex26S6vkPgh0Qll7fefJPzFy6wtb1NbzDgzJkzLC4uEpYPmtx86Lp8T5/69xSxDejHQ4wVBI5PGCiENAStTVZvXmJrZ0ipWiGamCTwCz/FyI1QjsVzBL7rUjWGai1i+bU3WNlZZ+zIEWLlY1TEwpjGVQYtD96J97UizQ25kZyfHMcThjByyHSPSApmIgdHJ+w0mriRxq8pJqsZWdhl3HN4/9YufVvn3JlTfGvzLk8dHysYhZmmJGM8R9NPIO0dPG3v9QesrKxTLh9D5wqsRkoHKRwmp2aQ0hJGDtPTR5iYOIIxCd3+LlYkQNFKcX2NcjOyVINwMEYUps5WkCTFcO/IzNTBC2NB5zlIUI4s+uO5RgDlchnXcbDW8IknHgEDD1/8OOmgy4985gJRdRGBIjJdarLHUNTA5jjKYXL2OO3mNr3W9j708KAY9u4yGGZI4eC4IVY4aJWicIu2TJ4W7qlG0mtusuhaJvwBwc4qUZIT1GEwNU4vNCS9Lk4u8DyXPE3JMUR+ziQ+3UN6nr04o1Kr0dptkBpLM4Z/e2OHxu0WP8Qsv7W8y1oCqRZoLfE8B8/zRiS2DCkKmQRJgWQKggApXHrdNv1+jzGrSZOURrdzYB6DborEsrAwQ8WDja1bUJJI38MMJQIXLQqtHeV6SOkghCLLUnZ2tri9dLvIx/U4urCA5xX3xVh9liQesrG5yuOPP33ozlOIwllrY71BJoc88YlHGawNeO/KGmstl0YS8P7yLm51nHOnPWbHA8rE1EpjtFqaK7dbdNO7nJwrsbm7znq7S/eVtylVa+SthDiNwSl26QeFlZY0T9lzUDK4KBSDdpOPPfwglShgfXUFkyc89NAZnv/sc4SlKrfW1viDb/wRrXaHEwvHefyRh3nu6ScJA4G2OcrzWVna5uqNWxjhkh/IPYATx08UyCvHod/v0el02N1tMBj06XW7OI6k0+3S7XXp9GPiJOfcmUWyPCNNUnwvYLxSQgrBraVlNjc2mZqeZjgccvfuXfygmBH044Prx158tO2UwCXJLRiLwSFQmmTYZ2OrTXl8jubbf8Qrv/ENzi5Mcf7CedZWN3j11Uv8wA88x0OPPcLmRoNWmrDa3uXG0hZHF45ROftJajOnSFUF7QQEjsE9xOh0rZkUUD4tePxChNQJVaXYNDnf+M4m77z3JlYcYe31Tcq1Xa5eb3Pq9Fne6ZaJ+9fBaH73j9epKkllbILvXFvjifkKzyyOk6TbaAEpDt3s4IGR0XDtyiozUzPUJhXKNQXk0I9GLDaPowtzjI3V8QMPa0rk8YOEoYdy+7T7y0jt4TgB2gyIfA+jFdVaGaUEnU4Po2F65uCTyaDfwRiN6zrUozEwBtdxUE6IlJIj09Ps7LbJcs3/9D/8j5x+4hk6WyvMhZ8lSWF7bRXT2WRC9lixlWJn5LmMV0o0j5ygtb12KNwSQEcZSAeMIDUDHFRBi5YxLeOQaV2wWaVDy82Jc8t/9tgi7600MVIx4VWo4kIck3gu6RjEgwwdxhirqHqCzqBDNjiY7l6rlHEcn7G6y+///mv4UY3P/Zkf5ey5R9nZ3SIctCmvdRl2NXm/Q6UUMox7NNu7ZCajFLoYbciTIWceOkOj0SVPBxhrKVUrrK/eYn4y4OlnLhyYhwxCttaWaO5s8MzTxxm6CUF5nKP1eYaDNpdffRGtJBMzs6jcwRrY3GySZTl5rgmiEjgOvnRQorCGq1bGEUgau1uMK5+v/eHXCYKIhy88ds88XKHxQodzD8xz56kdFhbrNBoxzz4xwanPnue1f/xNTn32Qd54+y7vvNHgwc0yP/TZOTqNnH/z8i1e38h4Z3ubZ1HUHnD4uz/1U/zMP/pNLr/7Pk8+eo5OM8UtJSjnYD5Dvz8YwW1drNGYPCUQKT/+w8/zEz/yg/yLn/85FqfrlFTOj/+FH+bCQ2f55re+ycULD/LFL3wWaR3yrJBrMBbeeOsKv/Ybv823X3qNTpxx9MyDKFkwPA6KwXAwgokqrl2/jOM4/PN//r8S9we4jsORI0dwZdGn73a7KOWR5TlpmuL5PlGpRNzdxVOSRqOBpxR3797FcQq8ehzHLC8vs7y6xj/6+//LgbnAR027d12sFIWwlMhxlUtpZoF2p8f8wiKX/u0f8tTjj1AuBQjP5dbyEkGokMrw9luXGA4N/UGMTvqUgoDjjz9LsHCBTDlECHxfoRyFUAcf23VeuK+nSc6glxV0ftelHwvmfJf3d6/T6MY89kPP47hDfCmplAxxDjNTY8yPB0ShS5xoxqWmVvEI6dOQfXwT4kuNNClGH5yHFJJup8/tW8t85vQjxEnGMGnie+N88Qe+QK874KGHHmJycgqN5NaNW7x96SVyXaBIZmdncVTOcDjAdUNcN0J4gjiOSWJLEPgjRM8hAlgYwijEdbxihzPyCfWloFSu0up0iZOYV16/xHe+8wJhELJ44SLDYYrK+sRJzPbddUpjJaSVeGHx8lJK4vvFFD/XmuQQ2j3SElmBEUBusIHCEpMbiSMkmUkIlcUxlsQY0szS6PRJc4uvJDttgwoM5AN8YenpEje7KSJQRMInyVNIDQ/UDh44Z/GAxqDPIIG3r22xvvYW6ve+zcXHHuWv//X/hj/7xafZ2unz+199mUa7j+tW2NpcJc1ilO9i8pRhnjE5XkFnCdZIDCl5npPlKXE6IMsFy0u3D8xjbmYWz4DrGprNHpWpGuutHZa3N/Ejj7dvXmHuxCKB6RPv9snjnF43J9cCx/XJtEab4n7P8rQgwhhotXbp9bqcPjvBk1/4FPoQLZlhNgTrceETs4zNGzxruf72Bo+ecTj+yBTVesjzf/FpHvzYCl/7le9w6W4L98WrTB6tMgglj3/fg7z72gabwz5/7ieepT5nOffMHG/97h1qFZ/lKz0eeCxCHHKCdpxiboPVBI6kEnp86flP82Nf/gLdrVVuXruCoxSPPnqRB8+cIs9znnz8CYLAJY6HBR0+LJMZwU//zP/JpTfexRrBqTNncKMS/TwnSXPah7SXhM25c+sKWZ7S7XbxPI9Bv4/NNVIIGo0G9UoZJSRl36c/HNIbppRKdUoDTbvdwqtVePhjT3K70aLbbjEcFn61g2FMrnXhSSsPASTsXZfv6VP/nqJScgnykXqXdQqDVms4tniaqBxx5vgiTjyOCkNu377Nzm6fU8fn6fVS1lbvksYpjoBS6PPpH/1LTJx+GBNE+IUhPL6nkKowWz4ohDD4ysG6kpW1HV5+9RJlv0wsI6ZmJvgzP/os164p6qd8OoManaZgc7dNry/oNHNEnlCuGAJXE/R3KFdK+GXF1FjA6akJzpeXkLZfIEgOCCkKNujq6ibddkqtWuFjj5/BUXOcOnkKgFptDD8ICcIS05+YptfbZmN9kxdffIFbS0vUaxGVskO5YqnXq/iBT56l6FwSlULSZEi3dfCx3fOCQrvCFiSGLMsKoo/jcu3mHbr9PlmWUw5C8m6TOB6ycusmYXUSkyW4XkAtrLC13SZcWMDzIwrVQYMzgizmeX4o2UdnHq6MkUaQJyCVR54rDBmun+NpS0kJXEeRZoZcKLpb2wjtkggXB4Nbjtg2Ic1+g3Y8xISStA/Cz+mQU9Mx09WDi3h3kKPcCg+cOI3vfJXAjRmr17lz7U2+8Xu/gSgnKH+czcYWxib0+03yPEMpD095KKHJsozpqXn8yMeKMXLbp9PsovuaLO0RhmUmxssH5jFdmyDtdVhevcm5M2dpdbbZbu/gz9YoTVYZP30cZ7xK7GlkBCK3JGlKllpkbohKEQhIBym9XocJrYuNS7dDr9chDCNKURmlDhZIQ0I87DM9H3H06Bm+9XvL3F5t8bFPHWMoLNNzY1SnJM+ePsfCiRneff02/Y1Nps7P8uRDn+LaNcGl1/9fjCuYXhzHuikXnzzBpd+7zt27W5SPBThyjDhpHZiGzWKU4+BKl9ARXHzwFE9eeICXv/1HLN28ysz0FLOzc3zxi98P1tDptPE9n+EwwXN8tO9y/eYdvv7Nb/PGW+9SqUace+hBHn78UZbX1thp9hDK5/L7Vw7MY211mXa7w/vvv0+v19t/ZvI44cjMEbrdLtJa4uEQRwpmJidY39xm0B9ijWU4GHA3SVhvNKlXKyTDPnE8xDIiD2lNu92GQwa9e/GRFvFqSZFnI5lS4ezTsG2thJBQqo3RHQ7Q3QSlAk4snqZSkjR2ugz6OdbA1NF5zj/yMKcfe4Zc+YXuwYjFqUYDucM0OnxVkFmyYcrchXM479zgO7//AlnuYFzD3Mwkxx86ydHaGd5tDdjYVmQbTbJ2QjbQWONhK+CPxbiiT2vrFu3WKhvHTtA+cZazj3gcnZlA2INx0XtO6f1ezIsvvMWZsyc4eXKRB84+jeNAnqV02js4Aw/lONTrE0xPTzBer1Gv18AKtrc3eOGFr3KmOsnt2yvUqhUmpyaQErq9PkoUVPqDIk1TrC7ErEpBqRAiUy5/+K2XeP/KNWZnptnc2sTzPfK0g5YORnhsrC7hh2WGzR2SzoDV7S6LJxzCMCDPc9IsJYpKCFWsSZYd3OPrp11iLyfIfTwZIGOwviTPIbeGiu8yJgUoQ2Y8Yp1zfMJjZbPPRt9DuD6BMlRKVZabK7heIaVr3AzpFG0aXwsqhwx6zz7yNK50uLu8zIkFj/NnHiD0A3SasbH2CpWps7zx9hv0On2szuh1OhgpUI4kSQZUoxLjYYXxksKpVxhkMdWoRhi4NBqC0Cnx8NlZzp2ZPzCPWrXMlSs94liDcTk6e5btOGFrZ4tB2mf6oWPEWtNLMkpRRKUcsr5+E20UoRdRqlRI05hMQ7c7xGpNnuU0drexI1je5sYaeQ7T0/eemxhjcFwLdohyfbYbMW6txtyjp2mlGdNzE1TGHazqcupihdOPPk021DhehvIdfvcPV8itoTReA0egyZidq1Mbj3jl1bf5wWeeQSqHbHBwU3yyGuJ7HlGpRD1ymZuscenlP6azu41SkiNHFnj++S8wMTFNs9nc1xTKtObmjWVeevV1vvPKq2xsbjM5c4QnLp7j0889Q31inMXjs2zvdNFaMl45mPzU6XS5ceMGd+/eHckzJFQqFUp+SKVSYXl5mXqlQr1ex1pLVCpx9eZNoigiiiIcx9lXNnz2uWdp7OyQpilpkqK1Jk1Tdnd36fYO3nztxUdaxENXkNqCTu5JieM6OMpBiEJP4DNf+DyNu6t0mq0P3m5pjzSJkVagjWHi6FEWjx/HlCIkEnckWgMgXQ+pCsjiQZHEPXQOSax5/eXXOP/4BabCEut3Nui1GrQ3hkSDDuL2DdINGLaO8GPPRkwrGHSauL7g6koTne1SigSN7RY3t64xFStKiaAjTnOq6hBx8A5nTyPFWsGtm+u0mgPmj+5yZOYopVKJRqOFEBIlHfrDITMzcwwHuxidc/rkAtXKOF7wcaYm61y5+h3q9Rph5DIcdhBWFeJhjkIcssxKSoSEJIvxcw8rJN955RLXbt7iwQcepFYuI61lq7HLMMvA5HztX/49Hnj8Syw+9DC2Ns7Y4x/nxisv4js+rueRZdm+OFEaJ2RZRpYe/JD6vgYryUdCQkYlhXa66zKwhprIUUh6OiV1PUpKUlIBS6ub9HEpHYnIkyHVsMbi5BiRH5BbQzPLkSpE5R7VYQPXPfiYurq0xjtvvUolcnn0wnFazSbCWKwnKGnJjZtXWbrVQopiCJ0mQybrIb6rsDrlsUeP8bnnPo+wMZudmLX1lyAf5/jRMSbrlqzV4cSxGcYmDiFzSEGlUufc2ASeH5GlirNzjxJu3mFz8xbNbAs3CpEWBrGmlyVI5WBcRViKyNC4oSJtDWh3Be3O7uh+0yjlsLW1hZIeY/XJA9Mo2LDFM4oSuI5Lbcpj6uQUqWMp1UP8soc1CVnSQooBw0zjuQGd7SHvXb6DV/bAK7Gxs0GpGhHHhjhOCYMyZUeSZelITf/e8aUvfA4pJWEUsjA9jmcTdu4uMV4rE+eWjz/5NHMLizQaDYRwCIKA5bubvPDi67z4yhusb28zTGJcx+W5Tz7Dp599ikopIE5yKl6J8EiJOEnw5IkD83Bdl/HxcXZ2dthtNkmThE998pM8fO4CS3fucOnSJQLXJfB9XNel3+8Dhc644zhEUcTOzg47OztcfPg8aZKwvLxctEFzS5plaG1wDjshjeIjLeKRU/QzC7KOxpGF9reSIJWlvLjA3PyRfS0PKWUxyLIWBeSZRluDNTkaB4nAkR/aifteITx1yBBta2uTqckZers7/NG/+Q0+9vkv8OiFRxg/toixMXF7yO7aEj/zlb9DS9aZeOQ/4o/lOY5NZTS319A64Z3rDaoll5mjszS6JcTkeZYbIRtxiuuuMfvUOHMHt8ThQzKY1ih2tjtY4MrVN3F9h431NlFYIstydltNtrY2EDKh1VqnFFWR0sUgiYKIyfF5dhrr5GlaQPF0TBCEGC2YnDj4IXU9jzQZFlCvdMjt5XXurN5lYX6ei488Rq1Splop8+KrL9MZ5PTaDd576xJ//q98BRHWKeUZu2tL7KytcP6Jp7HGFHKduUbrHKM1OstxnYNvSmU8hLEYpUiTHByJNRopDJk2dBX/X3tn1mPZdZ7nZ6097zOfqlNzd3Wzi91kd5NNUSQlUZJlWZEDWdAQRM5N8lcC5Dp3RhAEDoJEjmHDkW0BmuKISiTZshyREklxEGf2wKrqmk/VGfe0hlzsw1YSgVW6CIg0sJ+bQgF1sWqfqm+v9a33e1+0sBR45FbhCp83XtrkaAzhfIzr+FiTY9IRj1x6HGk0w0lBM81xpUWkYwIjZ3ajH8xLz/4Dq2s9Vpa7HBztUGQaVRSMhyOEcYABjswI/Rjf82jFXS4supxbmcf3NXEj5mNPXeOgv8vzf/kdPnJ1jdu3j5lvOqwvdbATQaNRx8jT2zpWQ7c7T5anhPUWJ8Mx2sBS4zxeIXjxxecY2GNqzYh8OuX4OOXqQ5fZ2j3kaHBIvVMnasV4DcHhe0e8/c6brC6fKyWL04SlpRXW19fPHLs32qKUwfMicuWgdMLa+YB218c2YqRvMFKSTMFYF6xhkiQIx2N/a0w6OeKTv3uZ8SAhNwUxgp3tQ8ajnJoX060HpNkINz69HP2TL38BpRRJmhA5ltiB4NplhOMwThXdRp0kTQjCiDTNeG9rh29883u88PKbDMcZVpQtpgcvXeTpjz9J6Adoxb2AmCCQOFjcM16u/X6fJEnIixzfKy2c3333Jjfffpfjfv9e29D3fWq1GkprgiBAa81oNEIpxXA45PDoiM3NTZLp5J5Z3PHJEGuh1+txeHh46jre50Mt4ld6LZRWSCEp3HJKsDR5Lz1JLGZmEKRnhc2QKcoRfGMwnsZqNZuQM1hh7sWxSSnw/dJwXZ5hnhLVYoQw1JyMc2vn+K9/+h3+OvteaYdrwXUsrlMw1/H4yLXL3Nr6Ef/t1W0uX1/HMwnT3dc4vv0sfZljzp2n3VtlkBWkw2N29/a5+cYTmMlHuboe8s8/ffmUldhfBxeIchz15PCEn/3kF4yOD1hYWWQwKFUs/aMx3fYKMtzD8Q1pPiCZFqTplCxN8b0I348RBAh8hO/jB9BseqTp6ceyLEsxeY6VEqUMF84ts7q0yMLyAzywcYnVxQU2Hljn9vYOP372JXp1l3/5b74vQYz0AAAWXklEQVTFQGn+7tt/xfbmNo4bkGQaiWQ6nVIUBcPRiMl4MmurxBTF6TvxfJJhAkNhUupOHS91yGMHoRSRdVEWdqRGmSmkBscH/5ENam/v4DmWsOYhXA+jRuxvb2Omx6zNuayGgq2dQ7rmhJMEtrLTVRBXrl5ACMlgPAE3ImoJVJaSZAXSJtTnLyH8LUIz4sEHOlw632XjQpN2u4XnhYigzfzqg+yeTJmM+jTDlHOdIWl/yrne4zz48c/y9f/4dVYX5vnDU9aRZjkrKytsbm5xNLaMJinp+JDF+Tm0MiSZ4OBwAlsDXE+yvNTms5/6BM/+4mVu3bzLp574NEUw5nNf+R3+7b/6M7a299nfO2Fl5RwLC0vU4ka5+zvjwvnm7btICVEUEtWaGMYsXp5n8+QOTE9od9v0b55QX8iRTkCa5cTtiPFJwfe/9Raf/uIy51e6vPb8uzRrXZSG1nKToBdjR5osGCJMgE5Ob7cNTg6YDI4IpGFhZZVWq00YBBRFQa2mSac5z/38RX723AvcubPN9u4eO3t7nFtb5zOfeoonn7zBwuIcQRCU1rUI/CCcbRZtaUsrJe4ZJ7U/+qN/jQWWlpZp1NtIKbh9ZxOrNHrmf1Jv1Wk2G0RxxGA8YP3cKo50ePVXr6KtQUrL6KRPluQ4TsDq6gJheMTyyipHR4copbn20AfbA//vfMjqFDFznbU4DiB+HcFWGq+ClSCsxBqDFRIkKGPK0I7S8QZj5czlrvQaf9/21HPkvRix08jygloMcbeD008I6x65npJnBqslWZIjZcG1Jx+ld36JwrO89+6LbL1yC5MWOGoLUfQZE/PZL36VRpBTqCl33rnF9997i/HhFqP0Osemfeo6/s9EofKrMXD37i5RLIla5TRmmkCawNg7oRkKNu8cEEVhmRbiutTrdYajIaoYUa91sMYF5eE5PZKxT6+3ceo68kLhzp5jkadYAcNxxl9+698hXI+vfvEPKIqCnYN9dJHz4stvsfHC84zGGTt393n+hWeJo4ivfPVrFHkxc9HLKfKcNEvLISKt7pmKfRD1VpdC5YgswYQJLWeOwzyZyQwtqSjQThmh59UcrCdwXJeFuZjBOMcYhT87odniGPSQUV8xtQX5eMpxNmZv4tConXXBmiLdACvh5KBPp9UiyXM8x8UKn527U5Jhxvr5GtcfXmJtsc18J8YIB782z8bVj/HqG2/yx//hT9hYmqfRcHA8RZGlvPnKT3jidz6HG9VxgjN24kYzmU5ZWVvjpZd+RRS6DJOEwWCE70W0mm2isMbw+ITd3W1GDQ/P8WnVQq5ffYiF+UXGRmCnmrXlVZp1j6xIiII23YUWo+GYIJJY9/Q2xk9++hrWGoLAJ6wFLPWuU/N63Hr9TWrzE/7x5/8Q6e8ynbxHLsdYYfHDmMFwxPHgmM89dINskiKdBqFfZ6pGzC+FPPzoEoOf94kiCRSIM/TZzz73HJHN6ASShWYdU6sxylLSLKPbnePl117jm9/6G27e2WR374DpeMwX/+D3efrpj3Pp0kX8MCwze+9ZJ9vfyLwEznSX/OiNh/F9Hz8I2Tkqd+PDkxMGyaTsgTsRk+kYo3OiNCJJxmysreP7Pm+9+TrjLC/ta4u8TAZyXA4PD9nd2WFpcY4smSClUzqq/hZ8uPFsjihzD4VAOM6vg0lnVtiOdMuJKf2+SboFo5DWAKW7IaocPHCkBOGUbntaIz0JSpUOfmdM5PXmVwh8Hxs0iPZOcEXCXF1g6wHGCIrcxYiC1QvnybDIQCA5IEv7xG5ElhySD/bQOuY7f/51LAmpyklGCfv7IzregEwpxua3f7z2nhG9BSvZ2jzg2o0Njk+moGs0G12aLR+sZjScsL11F6ULhJUsLiywubVFlhZIEdKot5nrtvEWI1QRcPOMoZIXfvkrPvHkjXJHUiRoA2++c4txkuC5ij/9i28gpCTwPTzX5adv3OHGzTs4AsaTEz7/+1+gXm+yunq+PO4mCZPJhCxLGE9G5ecNZw5RFPmYTGlMLvClRMwCQawQpORI4SAN2EwjhQI35OBkjDAGrXKEKhCOQ56nLC20UI4gTQqOByPyScJUK7T0cL0zirhKSfIc6wjazRYCcCiHzYTXwHJEuy352BMbXLncwxEg3Ih6s0t74RIvvvQO3/zmn7Daa7K8PI/wLN25JiZNmAyPcAKXZqtBq3n6HEF3bh6E5bh/jCMN9XoD1VspvTq0ptlsIzCEvmB1tcXGxiq3bt3mZDhkfm2dvdEum3s3ae83uHhuDc/JOR5ZWisdltbnWFtaYqgOyOTk1HU0O00m4yleEDDJJ6xeWuCB3iN8+3/8gt2ju2zUj1ju9Hj5xWeZOod4vkN3rs3JiaTXa+IFkr3dCZNpwCTJKHSO68LFh5q8+as+YegxUVNydboQ4Pj4mPZ8k5O9u4z7R9SbLYajcTk631vkz/7ir3nxly+RZAVRXOPTj93gy1/6Aq4LSTLADz18P0DKMstAz6S076dxWVv+vZ11p/b41csIIdg7OOCd4228dhtUSpaMwJEYk3Hn1hhXCnq9HrV6nXa7zXA4RBtzL5NWCEEYBYxGY6IoIsszDg/2mAwHLCwucjI6PnUd7/OhFnFTWIwyM88aizIGbTRaSqwjkaZsoUgLRumyl5oVpXbZmLIQFAVSCFKlMUJgKM3ZsyzDccS9D2ht8bQ+cEBRCJK8YK5ZY7x/h6Q/IO52cWoxGA8/9FlZ7JH7DertGkX/OdwsI3My8rFLNnGRdszLP38OJUEKp3yYwmfU3yuTVc6YDPx1ll4pNbTWlLmaRpImGqUsjvQpColwMoLIYmXOtRsr7O+55IVCGI8wjMiKNlo5qEJgjUMyTRgMdtDGkqSnv9Tu3Nlm/dwaD1xcI1MpSivGkxGdTpOm53AwSkhSRZplpHnGNEn40Q+fwfckn/ns73H16g2MhSzLIS8YjUaznbhGZSnCDco0lDN8mlWeY63CdQPqooa1CscxOJ5EKkuuDIGdaYVzBWiO1ZRW5FIPPYwpKApIJkMuLq5z69YR6fCEyThB5AYjBK1ORL1xejtFCEsc+iSqIAw8ikRT5BlCWB66+hRTeZtbHckj1y/gB5I0yWh2F1G4fP+Z/86td+/w6MPrXL24CE6E8J1S5z4Zk7fnUNrHdwS9M1zqBqMp51YXeO2Vl5ibX8SLGzSdkGw8QSlFs95CqwHn1lZpNUN8Ac//8g3wQjoWrJMSxzGhbNFZ8tm9+y5pmrF6LqY2F5UKpuyYoTzdW+ejT11kPEqQ0uF4dIAbHNHtaR5/8jJ7Q5+D49eYX3iUhcU5+skEi2EyHHJ8lHPjIxc4GRzTHxyxd6wZjRbAFjg+uA3N/IM9ZBCUgTGnrgIajQZRXCOem2d76z2E7+MGIZ25RZTS5EXO5/7R7yEch6hW59zSAlt3NwHNgw9ulJJLnNIUzHVReYGezVxIKe/VJtc9vSyOB4e4jouHpteqMRwc4aiUbjPEj0I818W1DgKoRSGuEFy4cIGbN2/e89kPw4A4jmm3m0RRSHeuyw9/+ANMNuHyg5fotBq8u7V1xhMp+VCLuDAWXZQaWuG45EphsFhHIhyJLTRSSIxTtlOM0hSqQM3emPeCRRGl+kEIHMe9dxzyvNJD+yw98quvvoUxiqwouLDcYDo8wCjF0vnHiXpLJLNLkKDeKHuARyk23SfXOZkwWCWRfoAjfVwhyPIpVmVYBcbkOF5Clg7oH54RGgilNZst7wPCyMN1XIw1dOdi/DBDSg8/HCPcnFS5tDoWp1CsrM6TJhnGQpoULCw3yRIJ1mc8nrK73Sd0fbI8RZ5xy2215oVfvsLy0gJawfbWLtMkRdvSU6KMkyqXKhCEcUAr8nn4kRss9NY4PDxC6dKL2fO9mXNiQZalTCYTHnjoBrffeh2dn77ji2JDUpSuckLB2BYooZA6xNWa3EiktXRCD20dcmuZFJrAl9TiiCy3FCoHU9DpxPz4F/ssNOoMpzl+5CM9j+XFNk5w+ufSbHZIpymjoz6yU8PRAUiNF9W4cv1jPPf8c3zk+hKuY8qTW1GwtXWTJE3pNeHq5x8jDgMmwyG1Rh1jDa7jQiwRbg3fC5hrO2eeCPb2D5DSsLt3wNWFNVJVIKwly1JarSaddo2du+8RBQtkyZjNu4fc2drj/PolOmETLwM70OR+QVjzaXcXGZkMN4bYjUgzxTSf0rd7p67Ddca0Gg5Y6HaWiLVhd/AG8ZygV+tSUxOOBu/QmRN40xZaW4zRNC9pgtDhaKpZPO/gOCFbO5uQO+Ta4b2DPW4Px3xaXCLNHcRZBuvW0h+NubiywsHmu4xfe4VWt0cQ1/GCiH/2ta/i+h5bO3fZ399ne+cOa6srXLhwkcXlNVzXR+syAML3fZyZc6fW+t7mr1TvnH5iTNMc6Wg81+P65YvkeU6SJFin9DpX2pBmiiwvyLKM0WjM/v4+WinSNANHEscxy8vLLC0tlj4rh4eMRifUfZfrDz+MHwTceOjK6c/j/c/nt/qp/0cEXukn4cQxSAdtDWmeUWQGlWnG0wmF1kjXKZUnQhJHIdba0r1PKwpd5j7iSiTOvZQNKSXWlKEG8gyd+Mmgj9GaQim+/6Pv4RSaqNbkcGcHc7CLKhR5UfDMdxy+8k+/xujOJtrssLR8nvOXLuJHIUr6uEHMfG+e/vYdXv3Z39NstDk42OeJTz5Bp13HnGEBu7DcLe1jfbjx+Hnimou1DqoArEsQKWp1GA0FQhpGwylGtVAqIM3GBKHH8VHCwd4YKeFkMGR3Z8BokJa2o/MZYRhQr58+VCIdwXg05s+/8S0aFgrXQXsuQkAShRQ6mYVmlH/0rvT5/EdvsHjlBnGnS384YHKckmUJSmmKvHzpDgdDDve3uXh+Da3zMy0+bQ6RiskxjMcFyvPIhaaYJTn5ro81gqxI8D1wHcPtSciB1niBRCqDEBrXWv7Td59h4ORIL+LClVXefe+Yp65fwvMChoPBqevIc43jeKytnmeSjdCkbDz8CZ765Nc4ONzl3FKLMPaxM1uA5eVlhsMhtTgin05Ix30gpj7fIHQj0jSdhQcIrDT83Q++zXwzJoxOV4Vcv3qZwXDCkx9/mnoQcHzcp5DgdmOm0yEHB5uMJmPefDvl9q0d0kzw9Cc/w2OP3WBjY4Ob79xCd2qsrS6QFS5JNqR9sUWvNceF5hW04xDNPYY/PP3/JXTe9/o2mMKiPDhO9xCioBaXO9hxfoCbuShP4voe6sTQ6PgokdB2Ixa6DYLLIaNpxjQfMk5yrnZXuHbN4+X3XsQYidUeX7r2weu4eu0Rbt25yXf//hd84tErXL1yhXqzTVZonn3hRZ75wTOkRYYfBly+cplHH/k4G5cenNlYC6R4P76xfHm6nlsa7qmCyXRCvV4nzzLOGrGxXkRuDdILqPkecRzSqMc4wuJ7HoXSZEaQq9JDP0lT/uq//GeOj09mCUIh0+Ext6Yj/v0fv1NujMKQqw9vUA9DxsmUmhSE/ul2Ge/zoRZxmNmeSnB9B2Ml0hGkQiEcw+BwDykkoQhKUx5HMDoekOd5GcYrBI7vg7FkSU6SZURx+YtqVcp4sjwrdz2nrUEVZR/dWubOrTDfq6OMQzYbT89nAv47N2/x/e/+DUc7OzR6bcbFlNfeeh3P95BegB/GbN728ISkNrfIeDqh0VtEyZhXXnmtvJjlKx+4jvmlGn5Qiv5/+tMXqddjOt0mvd48cdzAzT2iep1W02E4GuBInywzJGlCUeRoIwlClwceXMXYhMUs4vz6AjvbI/a2x+wdDIijnKI4I25qFpNnhcQKsI5DUWgc16KtLZ+nUwYAG6NJkpRXnn+W+nyHhbU1rCuJohCrLaPRmGLmGbG9tYXVqrxtLxLOujEStvSOL8YKMlMG2QLS1PBqEq0lRTHhUBmYKBSWertGpnO8ROOlgrqvKayhMe/jewGCiOEo57Cfc3Q0QsgR6eT0E4El4+jwiE5vDqUV3c4aDz38u3hxxNs3XybwBUYp4nadMAqZTka/HuLozJEkSSmL1S65Kcftw7AM1J0kfYrkkEuX5hnnp7/kBZpmrcZ4UrC1tcX6+XWEKxhPJpycDGg2uziuy/7hPrXmHNcvPEi302apt8hCt8viJ+bJdEqSjlmYv8A0mfDW3RdwC59CORyNtxkeTFFnbHqSJMOVAdYaHD8AYQkjH6st2hYI6SDd0t7ZGkthCpSxOH4NbQukNhgESiuiOMSPJX6UYq2HK30WFi+itUXY0zW5x4MBtVqTa9cexXoed/b62N0+t2/f5tbt2zhhzMYDlzh3fp319XW6zQZq9oytBeHO7tBmJ3fplN8XRVH66EvB9u42m9t3ubL25Q9chx/4aGw5Ge54KGvB8cApZxoc18VXFumUObX1WszTT32Eo6MjilzheqVVrbUa1y/954MgwHU9/CCk0WjiOOXJ5bfhQy3i2pTKEmPF7IZY4EoXz0gQiv7BAZPpmCiKyfMCXWik47G7s4PnOPieT63ZQEpJmmaooih34tYSRxFm9g71/YCP3vjgo0gU+DMtOsRr57EqQwsXbVwcnJnJuwat0IWmvbRMd6mNnUkCpRAYMwsvlhIhPFwvQLpleIWWUbn2M1z73r35DiovW0KuG3G4X9A/2mNvr0+tFtJpN4jjFnNzHZQpqNXqOK6g3mpQFKCVochTxpMh2ozxPIkVikYzoBHXme/1iMLwN/Iq/28M5QkJa8kFFEWBAVzkLPrKKe2Ayx9Ba83LeyesHo5YmCb4gY9bd+gfHTOdTsmynL293VI+Kgyj/i6B7zOZnG48FRYxCJfQVzScgGyomRhJ3K3je5K9g0PsVOH5Hm6m6NZq5NMhse9CWtBo1Oj4EYORRHpQdxwOxgU7SpD6Mf/zjX0i39CKTj8uH530iZsNwjhikpzg+m2csM7Lr/6c4ckd2t0VcjUiT084d+4KRw4UWYHAMEkTtu/epdNs05vroayh3e7geT7GWILQ59FHFzB6SiM+YygtS7A2oN3uEPmlB43nu8RRTC2uURRTDo/69BZWWVlZo9Pu0u8fUYtj4iDEuJapGjDM+tx95x3Wli7jxYL927vcvdVnYHbZevuAVieEL33wOqTwgFIGrLVE2ALpAtaglCkdGy0YaXGFRLiSVKZM0hGZnuI5LsYotBG4QpTtJSmx1lAUUybJ0Wyw5fQ2l+f7eJ5XXgKOB+zu7fP666/T7/fpdrs8+thjnD9/nk67TRAE91KqoJQge543mxwuSonh7FIzmMWtbW5t8uO//VvefPtd/sVXPriILy0vo7XGkRIXj6IomEwmFEXpQuo4Zb6tdOy9/voDFx9g49IGRV6KEYy2pFmG8D0MdibUEEjpzmR4nOku+T7iN+Q1FRUVFRX3DWf7g1ZUVFRU/H9LVcQrKioq7mOqIl5RUVFxH1MV8YqKior7mKqIV1RUVNzHVEW8oqKi4j6mKuIVFRUV9zFVEa+oqKi4j6mKeEVFRcV9TFXEKyoqKu5jqiJeUVFRcR9TFfGKioqK+5iqiFdUVFTcx1RFvKKiouI+piriFRUVFfcxVRGvqKiouI+pinhFRUXFfUxVxCsqKiruY6oiXlFRUXEfUxXxioqKivuYqohXVFRU3MdURbyioqLiPqYq4hUVFRX3Mf8Lujorx8P+oqEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 70 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Mine\n",
    "classes = ['plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck'] # label对应的值分别为 0,1,2.....9\n",
    "num_classes = len(classes)\n",
    "sample_class_num = 7  # 每个类别选取7个样例\n",
    "for y, class_name in enumerate(classes):\n",
    "    idxs = np.flatnonzero(y_train == y)\n",
    "    idxs = np.random.choice(idxs, sample_class_num, replace=False)\n",
    "    for i, idx in enumerate(idxs):\n",
    "        plot_num = i * num_classes + y + 1\n",
    "        plt.subplot(sample_class_num, num_classes, plot_num)\n",
    "        plt.axis(\"off\")\n",
    "        plt.imshow(X_train[idx].astype(np.uint8))\n",
    "        if i == 0:  # i=0时,绘制该类图片的title\n",
    "            plt.title(class_name)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Subsample the data for more efficient code execution in this exercise\n",
    "num_training = 5000\n",
    "mask = list(range(num_training))\n",
    "X_train = X_train[mask]\n",
    "y_train = y_train[mask]\n",
    "\n",
    "num_test = 500\n",
    "mask = list(range(num_test))\n",
    "X_test = X_test[mask]\n",
    "y_test = y_test[mask]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Mine\n",
    "num_training = 5000\n",
    "mask = list(range(num_training))\n",
    "X_train = X_train[mask]\n",
    "y_train = y_train[mask]\n",
    "\n",
    "num_testing = 500\n",
    "mask = list(range(num_testing))\n",
    "X_test = X_test[mask]\n",
    "y_test = y_test[mask]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reshape the image data into rows\n",
    "X_train = np.reshape(X_train, (X_train.shape[0], -1))\n",
    "X_test = np.reshape(X_test, (X_test.shape[0], -1))\n",
    "print(X_train.shape, X_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5000, 3072) (500, 3072)\n"
     ]
    }
   ],
   "source": [
    "# Mine\n",
    "X_train = np.reshape(X_train,(X_train.shape[0],-1))\n",
    "X_test = np.reshape(X_test,(X_test.shape[0],-1))\n",
    "print(X_train.shape, X_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from cs231n.classifiers import KNearestNeighbor\n",
    "\n",
    "# Create a kNN classifier instance. \n",
    "# Remember that training a kNN classifier is a noop: \n",
    "# the Classifier simply remembers the data and does no further processing \n",
    "classifier = KNearestNeighbor()\n",
    "classifier.train(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We would now like to classify the test data with the kNN classifier. Recall that we can break down this process into two steps: \n",
    "\n",
    "1. First we must compute the distances between all test examples and all train examples. \n",
    "2. Given these distances, for each test example we find the k nearest examples and have them vote for the label\n",
    "\n",
    "Lets begin with computing the distance matrix between all training and test examples. For example, if there are **Ntr** training examples and **Nte** test examples, this stage should result in a **Nte x Ntr** matrix where each element (i,j) is the distance between the i-th test and j-th train example.\n",
    "\n",
    "First, open `cs231n/classifiers/k_nearest_neighbor.py` and implement the function `compute_distances_two_loops` that uses a (very inefficient) double loop over all pairs of (test, train) examples and computes the distance matrix one element at a time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(500, 5000)\n"
     ]
    }
   ],
   "source": [
    "# Open cs231n/classifiers/k_nearest_neighbor.py and implement\n",
    "# compute_distances_two_loops.\n",
    "\n",
    "# Test your implementation:\n",
    "dists = classifier.compute_distances_two_loops(X_test)\n",
    "print(dists.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x8c1a5f8>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# We can visualize the distance matrix: each row is a single test example and\n",
    "# its distances to training examples\n",
    "plt.imshow(dists, interpolation='none')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Inline Question #1:** Notice the structured patterns in the distance matrix, where some rows or columns are visible brighter. (Note that with the default color scheme black indicates low distances while white indicates high distances.)\n",
    "\n",
    "- What in the data is the cause behind the distinctly bright rows?\n",
    "- What causes the columns?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Your Answer**: *fill this in.*\n",
    "1.这代表,对于某一个测试数据而言,没有任何训练数据(先验知识)与它接近,这代表这个数据可能是一个噪声(这个测试数据四不像,什么都不是)\n",
    "2.这代表,对于某一个训练数据(先验知识)而言,没有任何测试数据与他接近,这代表这个训练数据可能是一个离群值(它严重偏离了固有的数据分布)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Now implement the function predict_labels and run the code below:\n",
    "# We use k = 1 (which is Nearest Neighbor).\n",
    "y_test_pred = classifier.predict_labels(dists, k=1)\n",
    "\n",
    "# Compute and print the fraction of correctly predicted examples\n",
    "num_correct = np.sum(y_test_pred == y_test)\n",
    "accuracy = float(num_correct) / num_test\n",
    "print('Got %d / %d correct => accuracy: %f' % (num_correct, num_test, accuracy))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "137 500 0.274\n"
     ]
    }
   ],
   "source": [
    "# mine \n",
    "y_test_pred = classifier.predict_labels(dists,k=1)\n",
    "\n",
    "num_correct = np.sum(y_test_pred == y_test)\n",
    "accuracy = float(num_correct) / num_testing\n",
    "print(num_correct,num_testing,accuracy)  #答案正确"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You should expect to see approximately `27%` accuracy. Now lets try out a larger `k`, say `k = 5`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y_test_pred = classifier.predict_labels(dists, k=5)\n",
    "num_correct = np.sum(y_test_pred == y_test)\n",
    "accuracy = float(num_correct) / num_test\n",
    "print('Got %d / %d correct => accuracy: %f' % (num_correct, num_test, accuracy))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "139 500 0.278\n"
     ]
    }
   ],
   "source": [
    "# Mine\n",
    "y_test_pred = classifier.predict_labels(dists,k=5)\n",
    "num_correct = np.sum(y_test_pred == y_test)\n",
    "accuracy = float(num_correct)/num_testing\n",
    "print(num_correct,num_testing,accuracy)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You should expect to see a slightly better performance than with `k = 1`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Difference was: 0.000000\n",
      "Good! The distance matrices are the same\n"
     ]
    }
   ],
   "source": [
    "# Now lets speed up distance matrix computation by using partial vectorization\n",
    "# with one loop. Implement the function compute_distances_one_loop and run the\n",
    "# code below:\n",
    "dists_one = classifier.compute_distances_one_loop(X_test)\n",
    "\n",
    "# To ensure that our vectorized implementation is correct, we make sure that it\n",
    "# agrees with the naive implementation. There are many ways to decide whether\n",
    "# two matrices are similar; one of the simplest is the Frobenius norm. In case\n",
    "# you haven't seen it before, the Frobenius norm of two matrices is the square\n",
    "# root of the squared sum of differences of all elements; in other words, reshape\n",
    "# the matrices into vectors and compute the Euclidean distance between them.\n",
    "difference = np.linalg.norm(dists - dists_one, ord='fro')              # F-范数,即矩阵对应的每个元素的平方和开根号,相当于计算dists和dists_one的L2距离\n",
    "print('Difference was: %f' % (difference, ))\n",
    "if difference < 0.001:\n",
    "    print('Good! The distance matrices are the same')\n",
    "else:\n",
    "    print('Uh-oh! The distance matrices are different') \n",
    "\n",
    "# 答案正确"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Difference was: 0.000000\n",
      "Good! The distance matrices are the same\n"
     ]
    }
   ],
   "source": [
    "# Now implement the fully vectorized version inside compute_distances_no_loops\n",
    "# and run the code\n",
    "dists_two = classifier.compute_distances_no_loops(X_test)\n",
    "\n",
    "# check that the distance matrix agrees with the one we computed before:\n",
    "difference = np.linalg.norm(dists - dists_two, ord='fro')\n",
    "print('Difference was: %f' % (difference, ))\n",
    "if difference < 0.001:\n",
    "    print('Good! The distance matrices are the same')\n",
    "else:\n",
    "    print('Uh-oh! The distance matrices are different')\n",
    "    \n",
    "# 答案正确"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's compare how fast the implementations are\n",
    "def time_function(f, *args):\n",
    "    \"\"\"\n",
    "    Call a function f with args and return the time (in seconds) that it took to execute.\n",
    "    \"\"\"\n",
    "    import time\n",
    "    tic = time.time()\n",
    "    f(*args)\n",
    "    toc = time.time()\n",
    "    return toc - tic\n",
    "\n",
    "two_loop_time = time_function(classifier.compute_distances_two_loops, X_test)\n",
    "print('Two loop version took %f seconds' % two_loop_time)\n",
    "\n",
    "one_loop_time = time_function(classifier.compute_distances_one_loop, X_test)\n",
    "print('One loop version took %f seconds' % one_loop_time)\n",
    "\n",
    "no_loop_time = time_function(classifier.compute_distances_no_loops, X_test)\n",
    "print('No loop version took %f seconds' % no_loop_time)\n",
    "\n",
    "# you should see significantly faster performance with the fully vectorized implementation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Two loop version took 46.859680 seconds\n",
      "One loop version took 73.628009 seconds\n",
      "No loop version took 0.419024 seconds\n"
     ]
    }
   ],
   "source": [
    "# Mine\n",
    "def time_function(f,X_test):\n",
    "    import time\n",
    "    tic = time.time()\n",
    "    f(X_test)\n",
    "    toc = time.time()\n",
    "    return toc-tic\n",
    "\n",
    "two_loop_time = time_function(classifier.compute_distances_two_loops, X_test)\n",
    "print('Two loop version took %f seconds' % two_loop_time)\n",
    "\n",
    "one_loop_time = time_function(classifier.compute_distances_one_loop, X_test)\n",
    "print('One loop version took %f seconds' % one_loop_time)\n",
    "\n",
    "no_loop_time = time_function(classifier.compute_distances_no_loops, X_test)\n",
    "print('No loop version took %f seconds' % no_loop_time)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cross-validation\n",
    "\n",
    "We have implemented the k-Nearest Neighbor classifier but we set the value k = 5 arbitrarily. We will now determine the best value of this hyperparameter with cross-validation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "k = 1, accuracy = 0.263000\n",
      "k = 1, accuracy = 0.257000\n",
      "k = 1, accuracy = 0.264000\n",
      "k = 1, accuracy = 0.278000\n",
      "k = 1, accuracy = 0.266000\n",
      "k = 3, accuracy = 0.241000\n",
      "k = 3, accuracy = 0.249000\n",
      "k = 3, accuracy = 0.243000\n",
      "k = 3, accuracy = 0.273000\n",
      "k = 3, accuracy = 0.264000\n",
      "k = 5, accuracy = 0.256000\n",
      "k = 5, accuracy = 0.271000\n",
      "k = 5, accuracy = 0.280000\n",
      "k = 5, accuracy = 0.289000\n",
      "k = 5, accuracy = 0.278000\n",
      "k = 8, accuracy = 0.263000\n",
      "k = 8, accuracy = 0.287000\n",
      "k = 8, accuracy = 0.276000\n",
      "k = 8, accuracy = 0.288000\n",
      "k = 8, accuracy = 0.270000\n",
      "k = 10, accuracy = 0.266000\n",
      "k = 10, accuracy = 0.296000\n",
      "k = 10, accuracy = 0.279000\n",
      "k = 10, accuracy = 0.283000\n",
      "k = 10, accuracy = 0.283000\n",
      "k = 12, accuracy = 0.261000\n",
      "k = 12, accuracy = 0.294000\n",
      "k = 12, accuracy = 0.280000\n",
      "k = 12, accuracy = 0.283000\n",
      "k = 12, accuracy = 0.280000\n",
      "k = 15, accuracy = 0.253000\n",
      "k = 15, accuracy = 0.290000\n",
      "k = 15, accuracy = 0.279000\n",
      "k = 15, accuracy = 0.280000\n",
      "k = 15, accuracy = 0.275000\n",
      "k = 20, accuracy = 0.270000\n",
      "k = 20, accuracy = 0.279000\n",
      "k = 20, accuracy = 0.279000\n",
      "k = 20, accuracy = 0.280000\n",
      "k = 20, accuracy = 0.284000\n",
      "k = 50, accuracy = 0.271000\n",
      "k = 50, accuracy = 0.288000\n",
      "k = 50, accuracy = 0.278000\n",
      "k = 50, accuracy = 0.269000\n",
      "k = 50, accuracy = 0.266000\n",
      "k = 100, accuracy = 0.256000\n",
      "k = 100, accuracy = 0.270000\n",
      "k = 100, accuracy = 0.263000\n",
      "k = 100, accuracy = 0.256000\n",
      "k = 100, accuracy = 0.263000\n"
     ]
    }
   ],
   "source": [
    "num_folds = 5\n",
    "k_choices = [1, 3, 5, 8, 10, 12, 15, 20, 50, 100]\n",
    "\n",
    "X_train_folds = []\n",
    "y_train_folds = []\n",
    "################################################################################\n",
    "# TODO:                                                                        #\n",
    "# Split up the training data into folds. After splitting, X_train_folds and    #\n",
    "# y_train_folds should each be lists of length num_folds, where                #\n",
    "# y_train_folds[i] is the label vector for the points in X_train_folds[i].     #\n",
    "# Hint: Look up the numpy array_split function.                                #\n",
    "################################################################################\n",
    "X_train_folds = np.array(np.array_split(X_train,num_folds,axis=0))\n",
    "y_train_folds = np.array(np.array_split(y_train,num_folds,axis=0))\n",
    "################################################################################\n",
    "#                                 END OF YOUR CODE                             #\n",
    "################################################################################\n",
    "\n",
    "# A dictionary holding the accuracies for different values of k that we find\n",
    "# when running cross-validation. After running cross-validation,\n",
    "# k_to_accuracies[k] should be a list of length num_folds giving the different\n",
    "# accuracy values that we found when using that value of k.\n",
    "k_to_accuracies = {}\n",
    "\n",
    "\n",
    "################################################################################\n",
    "# TODO:                                                                        #\n",
    "# Perform k-fold cross validation to find the best value of k. For each        #\n",
    "# possible value of k, run the k-nearest-neighbor algorithm num_folds times,   #\n",
    "# where in each case you use all but one of the folds as training data and the #\n",
    "# last fold as a validation set. Store the accuracies for all fold and all     #\n",
    "# values of k in the k_to_accuracies dictionary.                               #\n",
    "################################################################################\n",
    "for k in k_choices:\n",
    "    # 存储超参数k,交叉验证时得到的多个accuracy\n",
    "    accuracies_k_list = []\n",
    "    for i in range(num_folds):\n",
    "        # 测试集\n",
    "        x_test_folds = X_train_folds[i] \n",
    "        y_test_folds = y_train_folds[i]\n",
    "        \n",
    "        \n",
    "        # 训练集\n",
    "        X_train_folds_delete_test = np.delete(X_train_folds,i,axis=0)  \n",
    "        y_train_folds_delete_test = np.delete(y_train_folds,i,axis=0)\n",
    "        \n",
    "        # print(X_train_folds_delete_test.shape,y_train_folds_delete_test.shape)\n",
    "\n",
    "        \n",
    "        x_train_folds_k = np.concatenate(X_train_folds_delete_test,axis=0)\n",
    "        y_train_folds_k = np.concatenate(y_train_folds_delete_test,axis=0)\n",
    "        \n",
    "        \n",
    "        # print(x_train_folds_k.shape,y_train_folds_k.shape)\n",
    "        # raise RuntimeError(\"pause\")\n",
    "        \n",
    "        # 训练并预测\n",
    "        classifier.train(x_train_folds_k,y_train_folds_k)\n",
    "        dists = classifier.compute_distances_no_loops(x_test_folds)\n",
    "        y_predict = classifier.predict_labels(dists,k=k)\n",
    "        \n",
    "        # 计算准确率 \n",
    "        num_testing = y_test_folds.shape[0]\n",
    "        num_correct = np.sum(y_predict == y_test_folds)\n",
    "        accuracy = float(num_correct)/num_testing\n",
    "        accuracies_k_list.append(accuracy)\n",
    "    \n",
    "    # 超参数k:[acc1,acc2,acc3...acc5] (假设5折交叉验证) \n",
    "    k_to_accuracies[k] = accuracies_k_list\n",
    "################################################################################\n",
    "#                                 END OF YOUR CODE                             #\n",
    "################################################################################\n",
    "\n",
    "# Print out the computed accuracies\n",
    "for k in sorted(k_to_accuracies):\n",
    "    for accuracy in k_to_accuracies[k]:\n",
    "        print('k = %d, accuracy = %f' % (k, accuracy))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# plot the raw observations\n",
    "for k in k_choices:\n",
    "    accuracies = k_to_accuracies[k]\n",
    "    plt.scatter([k] * len(accuracies), accuracies) # [1]*5 = [1,1,1,1,1]\n",
    "\n",
    "# plot the trend line with error bars that correspond to standard deviation\n",
    "accuracies_mean = np.array([np.mean(v) for k,v in sorted(k_to_accuracies.items())])\n",
    "accuracies_std = np.array([np.std(v) for k,v in sorted(k_to_accuracies.items())])\n",
    "\n",
    "plt.errorbar(k_choices, accuracies_mean, yerr=accuracies_std)  # 平均误差的折线图+每个点上画的柱状条=标准差\n",
    "\n",
    "plt.title('Cross-validation on k')\n",
    "plt.xlabel('k')\n",
    "plt.ylabel('Cross-validation accuracy')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x11addc88>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x11addf98>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x11aef4e0>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x11aef860>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x11aef748>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x11aefeb8>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x11b0b160>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x11b0b518>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x11b0b828>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x11b0bb38>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<ErrorbarContainer object of 3 artists>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Cross-validation on k')"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'k')"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Cross-validation accuracy')"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Mine\n",
    "for k in k_choices:\n",
    "    accuracies_k = k_to_accuracies[k]\n",
    "    x_k = [k] * num_folds\n",
    "    y_k = accuracies_k\n",
    "    plt.scatter(x_k, y_k)\n",
    "\n",
    "# 每个超参数k的 误差均值,方差\n",
    "accuracies_mean = [np.mean(v) for k, v in sorted(k_to_accuracies.items())]\n",
    "accuracies_std = [np.std(v) for k, v in sorted(k_to_accuracies.items())]\n",
    "plt.errorbar(k_choices, accuracies_mean, yerr=accuracies_std)\n",
    "\n",
    "plt.title('Cross-validation on k')\n",
    "plt.xlabel('k')\n",
    "plt.ylabel('Cross-validation accuracy')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Based on the cross-validation results above, choose the best value for k,   \n",
    "# retrain the classifier using all the training data, and test it on the test\n",
    "# data. You should be able to get above 28% accuracy on the test data.\n",
    "best_k = 1\n",
    "\n",
    "classifier = KNearestNeighbor()\n",
    "classifier.train(X_train, y_train)\n",
    "y_test_pred = classifier.predict(X_test, k=best_k)\n",
    "\n",
    "# Compute and display the accuracy\n",
    "num_correct = np.sum(y_test_pred == y_test)\n",
    "accuracy = float(num_correct) / num_test\n",
    "print('Got %d / %d correct => accuracy: %f' % (num_correct, num_test, accuracy))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.139\n"
     ]
    }
   ],
   "source": [
    "# Mine\n",
    "best_k = 3  # 我的最好k为3\n",
    "classifier = KNearestNeighbor()\n",
    "classifier.train(X_train,y_train)\n",
    "y_predict = classifier.predict(X_test,k=best_k)\n",
    "num_correct = np.sum(y_predict == y_test)\n",
    "accuracy = float(num_correct)/num_testing\n",
    "print(accuracy)              # 较高的准确率 0.139"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
